{"as_of":"2026-08-05T22:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:14930e4f79ccb2c8d0cc703ded3ae41eb4b9765d5ae1f9ce4f9f7ea2ead8bc51","coverage":[{"denominator":124,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-23T23:53:19.702198Z","state":"measured"},{"denominator":161,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":161,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":61,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":61,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T22:10:41.599390Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":22,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2309.12284","last_updated":"2024-05-03T17:36:07Z","snapshot_observed_at":"2026-08-02T15:00:50.388422Z","submitted_at":"2023-09-21T17:45:42Z","title":"MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models","version":4},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-05-13T10:07:53.748795Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2309.12284"},"observation_digest":"sha256:0c135d42252ef20fa8f6397d85a6a35328a9a328a7ab72a23a26294ae8156533","observation_id":"85b85026-1180-401e-b453-cebdd2a380ac","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2310.14768","last_updated":"2023-12-05T11:43:54Z","snapshot_observed_at":"2026-07-06T16:37:03.248953Z","submitted_at":"2023-10-23T10:12:23Z","title":"Policy Gradient with Kernel Quadrature","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-24T06:22:19.515535Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2310.14768"},"observation_digest":"sha256:4f0b3ecbba7e76dabd01b8a950ca6897919555a529b1dc5637eb7df85d93d226","observation_id":"9f66e892-e0a1-4fef-af8b-b54b884e27b4","resolution":{"observed_at":"2026-05-24T06:23:59.910612Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2406.10861","last_updated":"2024-06-16T09:12:16Z","snapshot_observed_at":"2026-07-06T18:31:40.910349Z","submitted_at":"2024-06-16T09:12:16Z","title":"Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions","version":1},"reference_index":156,"source":"pdf_text","source_observed_at":"2026-05-23T23:47:28.874336Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2406.10861"},"observation_digest":"sha256:06cff0a9d9abf2ac786fdcc0cef86d5f8140ff947a09ac9480de16ac0e7a4729","observation_id":"605ce5e6-6d0e-4d83-b373-03a78e3f3f3b","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2411.16312","last_updated":"2026-04-21T13:00:45Z","snapshot_observed_at":"2026-08-01T08:50:42.828998Z","submitted_at":"2024-11-25T12:01:57Z","title":"EPS: Efficient Patch Sampling for Video Overfitting in Deep Super-Resolution Model Training","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-23T16:47:31.372505Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2411.16312"},"observation_digest":"sha256:30863f5d817c2202cc3a424f1ffde624ebaa04ae7c17b8909d6d1e0a8e2ede18","observation_id":"0b47738f-c89b-49a8-a400-183c33a218c1","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2506.01942","last_updated":"2026-04-19T07:44:45Z","snapshot_observed_at":"2026-07-06T21:35:12.872021Z","submitted_at":"2025-06-02T17:56:02Z","title":"OD3: Optimization-free Dataset Distillation for Object Detection","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-19T10:46:09.642068Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2506.01942"},"observation_digest":"sha256:cfdb649d210504a35cf93895296a3baa4fdebb510381683dc60f7b491e37d5be","observation_id":"1df28b37-9ed3-4128-ab05-0bcb13d5ec7d","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T15:42:50.401881Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.19659","last_updated":"2025-08-27T08:16:59Z","snapshot_observed_at":"2026-08-05T15:42:49.718544Z","submitted_at":"2025-08-27T08:16:59Z","title":"SCAR: A Characterization Scheme for Multi-Modal Dataset","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T15:42:50.401881Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2508.19659"},"observation_digest":"sha256:c3db09677437ff369eec63bb5078f689d904c16bbd683f83c52bd3392e48cf65","observation_id":"b8dacc0c-1c84-4600-b188-cb68c256fca7","resolution":{"observed_at":"2026-08-05T15:42:50.401881Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-04T23:11:10.523640Z","title":"Y., Torralba, A., & Efros, A","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.07049","last_updated":"2025-09-08T15:24:35Z","snapshot_observed_at":"2026-08-04T23:11:08.796040Z","submitted_at":"2025-09-08T15:24:35Z","title":"Enhancing Classification of Streaming Data with Image Distillation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T23:11:10.523640Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2509.07049"},"observation_digest":"sha256:e8f4a935c0b05afee083f26daa9b84ffe273759908adfa410ba00a5b97997d3e","observation_id":"72c5be59-104f-4504-ab3b-cda103d5fe8b","resolution":{"observed_at":"2026-08-04T23:11:10.523640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-04T17:57:56.091542Z","title":"Wang, J.-Y","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.10367","last_updated":"2025-09-12T16:00:49Z","snapshot_observed_at":"2026-08-04T17:57:44.856181Z","submitted_at":"2025-09-12T16:00:49Z","title":"A Discrepancy-Based Perspective on Dataset Condensation","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-04T17:57:56.091542Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2509.10367"},"observation_digest":"sha256:77d11879882e1397a24cf341aaf9705bb94e99ccf5ec3fc9e5efa7af53b6064d","observation_id":"67f88919-e72b-4718-ad8d-e74870680dee","resolution":{"observed_at":"2026-08-04T17:57:56.091542Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-04T17:13:53.014085Z","title":"Dataset distillation.arXiv preprint arXiv:1811.10959, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.11047","last_updated":"2025-09-14T02:22:16Z","snapshot_observed_at":"2026-08-04T17:13:50.655746Z","submitted_at":"2025-09-14T02:22:16Z","title":"Data-Efficient Ensemble Weather Forecasting with Diffusion Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T17:13:53.014085Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2509.11047"},"observation_digest":"sha256:d4b58cc8a92c906a29e8a980ab2f8dfe9b7f90e38461534bf15b264992a31362","observation_id":"458086bb-c5ae-4495-9243-fb69b20cb543","resolution":{"observed_at":"2026-08-04T17:13:53.014085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-04T13:24:16.003251Z","title":"Dataset distillation.arXiv preprint arXiv:1811.10959,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.00866","last_updated":"2026-06-24T09:23:10Z","snapshot_observed_at":"2026-08-04T13:24:11.908564Z","submitted_at":"2025-10-01T13:15:15Z","title":"Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T13:24:16.003251Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2510.00866"},"observation_digest":"sha256:d00f09a0dbbaa6d113f004573ab86cb6072e702e5076cc9a514245eafd3acbc0","observation_id":"921ee0b3-5867-4f66-8493-f4513abe37fd","resolution":{"observed_at":"2026-08-04T13:24:16.003251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2510.17421","last_updated":"2026-04-03T07:59:32Z","snapshot_observed_at":"2026-07-06T22:33:34.164422Z","submitted_at":"2025-10-20T11:04:09Z","title":"Diffusion Models as Dataset Distillation Priors","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-18T06:04:34.300960Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2510.17421"},"observation_digest":"sha256:dfd5aab0fd35042e91ddf5878141f8b01b13280f4236b5b5ed7e200deff8a241","observation_id":"8c4fb6d7-ebf9-4f63-977b-8dc4aa1d74ee","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-03T15:50:35.326186Z","title":"Dataset distillation.arXiv preprint arXiv:1811.10959,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.15647","last_updated":"2026-05-30T01:41:06Z","snapshot_observed_at":"2026-08-03T15:50:34.331088Z","submitted_at":"2025-12-17T17:54:20Z","title":"Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T15:50:35.326186Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2512.15647"},"observation_digest":"sha256:b496a012fa26f4daa762eb0e4e20bcdda79eeae789bbf4e3a11c5aef3d6eeaec","observation_id":"de85da63-9a5b-4914-af35-fa067d982cfb","resolution":{"observed_at":"2026-08-03T15:50:35.326186Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-03T08:49:22.660051Z","title":"Dataset distillation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2601.15829","last_updated":"2026-07-03T14:31:22Z","snapshot_observed_at":"2026-08-03T08:49:21.047282Z","submitted_at":"2026-01-22T10:30:32Z","title":"Towards Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusion","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T08:49:22.660051Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2601.15829"},"observation_digest":"sha256:ba953257f1a74c3e5ef8e4aa9dba4fd3c1b83c2081eefb239661970c04922abe","observation_id":"66860eca-d307-405d-9572-55a23bd77f31","resolution":{"observed_at":"2026-08-03T08:49:22.660051Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-07-14T20:55:11.027028Z","title":"Dataset distillation.arXiv preprint arXiv:1811.10959,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.14830","last_updated":"2026-07-03T09:25:19Z","snapshot_observed_at":"2026-08-05T07:41:02.029543Z","submitted_at":"2026-03-16T05:14:34Z","title":"Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T20:55:11.027028Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2603.14830"},"observation_digest":"sha256:2f5bacfc931e35fa76d11a4692c134527956b66879e130ce4e863e84a2b5fc7e","observation_id":"b27e129f-135d-4f49-bef8-fe761d18d523","resolution":{"observed_at":"2026-07-14T20:55:11.027028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-07-13T18:26:04.651956Z","title":"arXiv preprint arXiv:1811.10959 (2018) 2","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.25144","last_updated":"2026-06-27T13:22:37Z","snapshot_observed_at":"2026-08-05T04:59:57.719569Z","submitted_at":"2026-03-26T08:03:45Z","title":"FD$^2$: A Dedicated Framework for Fine-Grained Dataset Distillation","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-13T18:26:04.651956Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2603.25144"},"observation_digest":"sha256:e6dd831845598ea0528cd7ebb06d099acf77797cc6bc07423f1501c6559f7e2d","observation_id":"e29122e7-e3dd-4fbc-a1c2-bda120aa5048","resolution":{"observed_at":"2026-07-13T18:26:04.651956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2603.26093","last_updated":"2026-04-17T00:02:56Z","snapshot_observed_at":"2026-08-02T11:03:18.306126Z","submitted_at":"2026-03-27T05:47:48Z","title":"ROAST: Risk-aware Outlier-exposure for Adversarial Selective Training of Anomaly Detectors Against Evasion Attacks","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-14T23:24:12.883229Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2603.26093"},"observation_digest":"sha256:19aaa4c6dea827c330540cd218d7e226013fdf17ecc1af7b177620b6f5dafbd8","observation_id":"1dfb524e-98c4-478a-a188-826738904949","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2604.07940","last_updated":"2026-04-09T08:00:22Z","snapshot_observed_at":"2026-07-06T22:57:09.435331Z","submitted_at":"2026-04-09T08:00:22Z","title":"A Systematic Framework for Tabular Data Disentanglement","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-10T18:27:39.046005Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2604.07940"},"observation_digest":"sha256:c70238ebbe26cf8efbcb190d5e535e98b2ba1b25ee967a0e3fa04ad8e75b0f11","observation_id":"412188ef-4683-4605-b016-4ef9803b55e0","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2604.10666","last_updated":"2026-04-12T14:47:41Z","snapshot_observed_at":"2026-08-03T00:41:31.043018Z","submitted_at":"2026-04-12T14:47:41Z","title":"Omnimodal Dataset Distillation via High-order Proxy Alignment","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T16:15:46.835576Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2604.10666"},"observation_digest":"sha256:fe6b136397f5155807d41469dbbdc48977951b7a37529adcfccdef86f4c86ce8","observation_id":"12de2a8c-3fa0-44b8-a4a1-f66bc1fb5136","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2604.12941","last_updated":"2026-04-14T16:35:04Z","snapshot_observed_at":"2026-08-02T20:19:44.512770Z","submitted_at":"2026-04-14T16:35:04Z","title":"Direct Discrepancy Replay: Distribution-Discrepancy Condensation and Manifold-Consistent Replay for Continual Face Forgery Detection","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-10T14:50:39.077649Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2604.12941"},"observation_digest":"sha256:74a00e0240b36ab89b2b5fb50e9517bed8a03b7a74e053203f9d1b466cc1fa3a","observation_id":"600fe040-e783-4514-accc-33879221d4f8","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2604.18135","last_updated":"2026-04-20T12:02:02Z","snapshot_observed_at":"2026-07-06T23:05:04.279268Z","submitted_at":"2026-04-20T12:02:02Z","title":"Soft Label Pruning and Quantization for Large-Scale Dataset Distillation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T05:44:25.808155Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2604.18135"},"observation_digest":"sha256:8fa347e004855283b3b2c698b22b360cfbf0b8c95ae93dbd88b8bf350bdacc8e","observation_id":"4f770905-e6d9-4746-89c9-a70c15b094f1","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2604.21952","last_updated":"2026-04-23T05:27:39Z","snapshot_observed_at":"2026-08-02T07:47:00.163884Z","submitted_at":"2026-04-23T05:27:39Z","title":"Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-09T23:02:07.554154Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2604.21952"},"observation_digest":"sha256:e3815111d4db31fc856c1533baa1be34799a60a9a80acb3f8f1ba2f9ca758f46","observation_id":"fa98f8fa-ec7e-49fd-bd10-8b3cfc0423f4","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.00185","last_updated":"2026-04-30T20:03:21Z","snapshot_observed_at":"2026-08-03T16:47:51.492683Z","submitted_at":"2026-04-30T20:03:21Z","title":"Fair Dataset Distillation via Cross-Group Barycenter Alignment","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-09T20:42:58.995723Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.00185"},"observation_digest":"sha256:69df27d218d474206227d037526440094e8c9f13a7596642d5cbcf39a78bef4d","observation_id":"8286c3c9-ea6e-4601-91bb-963d0de18a50","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.00578","last_updated":"2026-05-19T09:34:37Z","snapshot_observed_at":"2026-08-01T11:36:14.208999Z","submitted_at":"2026-05-01T11:25:54Z","title":"Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-05-21T00:13:11.388212Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.00578"},"observation_digest":"sha256:11f9b424d3cc9074d605cecfe2aa0ef9941812c07eae3562bf48718a489aaf5b","observation_id":"caa756ec-f51f-4272-9011-05e297537b7f","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.00832","last_updated":"2026-03-30T02:52:21Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-03-30T02:52:21Z","title":"Synthetic Designed Experiments for Diagnosing Vision Model Failure","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-14T22:29:30.032510Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.00832"},"observation_digest":"sha256:ba55a01298e515b009dae7a3f3b9cc60d78b88347f8f9551b988b403c74e1be2","observation_id":"3a1d8e67-18b0-4e9b-aed6-f71b48efbd71","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.04569","last_updated":"2026-05-28T14:50:27Z","snapshot_observed_at":"2026-07-06T23:17:18.486586Z","submitted_at":"2026-05-06T07:15:29Z","title":"LIVEditor-14B: Lightning Unified Video Editing via In-Context Sparse Attention","version":1},"reference_index":284,"source":"arxiv_source","source_observed_at":"2026-05-08T16:38:20.057250Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.04569"},"observation_digest":"sha256:33f70e192e01f9d312e369a38e6b99dd2a45a06c82d47c374f31b9deee55f10c","observation_id":"c02fe83c-570b-49e8-a070-733566927de6","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.07194","last_updated":"2026-05-08T03:41:51Z","snapshot_observed_at":"2026-07-06T23:19:35.885430Z","submitted_at":"2026-05-08T03:41:51Z","title":"Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-11T02:35:54.619698Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.07194"},"observation_digest":"sha256:8e580a4dc1b9e38caa87493d2bac103793c69d44b9868197ae3de9e1c07a9471","observation_id":"0e509c94-c468-4080-9912-caf530f47eb5","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.08616","last_updated":"2026-05-09T02:19:31Z","snapshot_observed_at":"2026-07-06T23:20:47.880233Z","submitted_at":"2026-05-09T02:19:31Z","title":"Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-12T00:52:25.736799Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.08616"},"observation_digest":"sha256:80c2c5489a777d89935d955320680735eb9c67cd271d38f8f0ac316131f000ba","observation_id":"fa457995-920f-4826-a3f5-ae213fc7eb52","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.12649","last_updated":"2026-05-25T06:52:53Z","snapshot_observed_at":"2026-08-04T00:56:32.436112Z","submitted_at":"2026-05-12T18:55:53Z","title":"DIVER:Diving Deeper into Distilled Data via Expressive Semantic Recovery","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-05-14T21:15:07.466245Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.12649"},"observation_digest":"sha256:0a7c2d054dc946c4b53d103ebdff867e8148a08700812e8b1c7ee3e1b96b414b","observation_id":"71ec5b80-0003-47c4-a9f0-d25ba35f2eb1","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.12942","last_updated":"2026-05-15T00:55:20Z","snapshot_observed_at":"2026-08-02T23:37:05.142968Z","submitted_at":"2026-05-13T03:23:35Z","title":"From Compression to Accountability: Harmless Copyright Protection for Dataset Distillation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-14T18:48:17.188156Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.12942"},"observation_digest":"sha256:10b6c991b4fac6c4d2db19a03e97ac06836d70de5276a2003f7484e4e929a92d","observation_id":"09e5cdbd-8774-47d6-bf85-610a0a1bae90","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.12942","last_updated":"2026-05-15T00:55:20Z","snapshot_observed_at":"2026-08-02T23:37:05.142968Z","submitted_at":"2026-05-13T03:23:35Z","title":"From Compression to Accountability: Harmless Copyright Protection for Dataset Distillation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-19T14:22:25.779713Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.12942"},"observation_digest":"sha256:eba7801eddd58573026bb5d79a88ad118f30c4db724e435b72e728789b7e8149","observation_id":"2b9f6c15-5333-49c5-8e0b-98c68fd829d6","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.18012","last_updated":"2026-05-18T08:05:46Z","snapshot_observed_at":"2026-08-03T01:01:57.336345Z","submitted_at":"2026-05-18T08:05:46Z","title":"SAS: Semantic-aware Sampling for Generative Dataset Distillation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-20T12:19:42.382196Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.18012"},"observation_digest":"sha256:38c244d7d8a6467d6dc4f696608674edddac151187d2d1dc58c489a0d2e176bd","observation_id":"36347848-2f42-48e3-bdce-a1843c4b4ef6","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.18836","last_updated":"2026-05-13T02:33:38Z","snapshot_observed_at":"2026-08-02T11:10:27.314624Z","submitted_at":"2026-05-13T02:33:38Z","title":"Spectral Gradient Surgery for Domain-Generalizable Dataset Distillation","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-20T20:41:09.235036Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.18836"},"observation_digest":"sha256:2e224e5222980eb6170724af8f0b1973df64f66bc826968b5ac6b6564e7ab4cc","observation_id":"22eedf24-8210-485a-af90-d9bcce78cc06","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.18893","last_updated":"2026-05-20T06:17:21Z","snapshot_observed_at":"2026-08-02T09:01:25.518839Z","submitted_at":"2026-05-17T07:08:22Z","title":"Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-20T15:17:23.832754Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.18893"},"observation_digest":"sha256:26be3db649a31b8a08c978adea3fab127efcb869e9d8af7c0e3fd19dc08fa29b","observation_id":"c61b14ec-00fa-4c77-9738-56eba9a69ccd","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.18893","last_updated":"2026-05-20T06:17:21Z","snapshot_observed_at":"2026-08-02T09:01:25.518839Z","submitted_at":"2026-05-17T07:08:22Z","title":"Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-22T00:58:31.100417Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.18893"},"observation_digest":"sha256:92c03c9ce24109e3f723a57e1c5d00039e5899797550a06bfa8c939c4b908e23","observation_id":"ace383db-36a6-4e5e-8feb-c7058758cd88","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.20606","last_updated":"2026-05-26T06:39:07Z","snapshot_observed_at":"2026-08-01T20:14:00.779648Z","submitted_at":"2026-05-20T01:49:39Z","title":"Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust?","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-30T17:58:35.879475Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.20606"},"observation_digest":"sha256:bf002908e9c70ad57dad07aae61fcb9d47bfb46f9342f635cbdaed0a9eaede9f","observation_id":"8ec97958-9392-4b06-b2e8-2c7e80290242","resolution":{"observed_at":"2026-06-30T18:04:58.276129Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.21765","last_updated":"2026-05-20T21:57:21Z","snapshot_observed_at":"2026-07-06T23:32:10.472558Z","submitted_at":"2026-05-20T21:57:21Z","title":"Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-22T09:22:47.640653Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.21765"},"observation_digest":"sha256:3b7be72e422ac09e5d9649ebde727be6687395f7799d11fd623df3f392e7743c","observation_id":"04a91a02-69a8-4267-afc9-344a65df89a0","resolution":{"observed_at":"2026-05-23T23:53:20.506326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.23482","last_updated":"2026-05-22T10:41:58Z","snapshot_observed_at":"2026-07-06T23:33:39.204744Z","submitted_at":"2026-05-22T10:41:58Z","title":"Multimodal Distribution Matching for Vision-Language Dataset Distillation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-25T04:30:04.849603Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.23482"},"observation_digest":"sha256:446ecfa21626ea6bdbf0ef0cc0cb05baf99da0531bff534bc19f6cc89713ffaa","observation_id":"b29b9174-524e-43dd-bc17-c32bb6546301","resolution":{"observed_at":"2026-05-25T04:30:19.758454Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.25022","last_updated":"2026-05-24T12:01:38Z","snapshot_observed_at":"2026-08-04T09:45:11.594229Z","submitted_at":"2026-05-24T12:01:38Z","title":"D3S2: Diffusion-Guided Dataset Distillation for Semantic Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-30T12:21:56.765731Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.25022"},"observation_digest":"sha256:68ad846660c7b936c2c2ed8aac0c67dd4be107fb5e4eb10217e1df99c5ce1ea3","observation_id":"86e76c8f-3f32-41ee-b2e4-fbea389154cb","resolution":{"observed_at":"2026-06-30T12:24:39.687655Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.30772","last_updated":"2026-05-29T03:00:23Z","snapshot_observed_at":"2026-08-05T17:25:52.264745Z","submitted_at":"2026-05-29T03:00:23Z","title":"FOSTER: First-order Dataset Distillation for Text-based Sequential Recommendation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T21:23:35.783182Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.30772"},"observation_digest":"sha256:9a77712d9930996f1342c43de7f1b1220152510648bdd1bcc158777a8cd9adf8","observation_id":"21972875-c31b-46d3-b978-1221f0f97d47","resolution":{"observed_at":"2026-07-01T20:16:12.023134Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2605.31016","last_updated":"2026-05-29T08:49:17Z","snapshot_observed_at":"2026-07-06T23:40:12.939143Z","submitted_at":"2026-05-29T08:49:17Z","title":"An Efficient and Scalable Graph Condensation with Structure-Preserving","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T00:03:13.278727Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2605.31016"},"observation_digest":"sha256:24b2992385d64b1c3fef45fda74ae3f4e895bfe22059762b0237006570ed34c3","observation_id":"bfab1cc5-5d40-49b1-8ba3-d63b99118618","resolution":{"observed_at":"2026-06-29T00:12:50.278636Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2606.01920","last_updated":"2026-06-26T02:51:43Z","snapshot_observed_at":"2026-08-02T21:15:37.301148Z","submitted_at":"2026-06-01T08:56:47Z","title":"Pool-Select-Refine for Allocation-Aware Generative Dataset Distillation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-28T15:36:02.976349Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2606.01920"},"observation_digest":"sha256:fd5e579f79fe7b403153db060c2c8a5dbe27f930affb7fb0295c47e75734a859","observation_id":"f11f03bb-6c5b-4c1c-a512-a5bbcf913d38","resolution":{"observed_at":"2026-07-01T22:16:16.284269Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2606.03839","last_updated":"2026-06-02T16:20:02Z","snapshot_observed_at":"2026-08-02T09:22:53.577884Z","submitted_at":"2026-06-02T16:20:02Z","title":"Text-attributed Graph Condensation via Text Selection and Attribute Matching","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-28T10:42:08.372934Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2606.03839"},"observation_digest":"sha256:12c06201f0be98200858b6de3c0599baccc60f98a7c392767af75ef84339bd5b","observation_id":"dfab654f-7385-4231-a551-712f4adb663a","resolution":{"observed_at":"2026-07-02T02:46:28.144018Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2606.05883","last_updated":"2026-06-17T13:39:13Z","snapshot_observed_at":"2026-07-06T23:45:47.161248Z","submitted_at":"2026-06-04T08:53:58Z","title":"Geometry-Aware Dataset Condensation for Diffusion Model Training","version":2},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-06-28T02:07:54.718436Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2606.05883"},"observation_digest":"sha256:6115bcc4c531a9f1e914ed27e225752a910647ae399a68af720d4b6a448a6631","observation_id":"4f9058a5-77e0-47ca-9eaa-a0cd3d0df9b7","resolution":{"observed_at":"2026-07-02T12:26:56.834328Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2606.12243","last_updated":"2026-06-10T15:45:18Z","snapshot_observed_at":"2026-08-02T04:35:32.429873Z","submitted_at":"2026-06-10T15:45:18Z","title":"VIA-SD: Verification via Intra-Model Routing for Speculative Decoding","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-06-27T09:38:43.443489Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2606.12243"},"observation_digest":"sha256:700be47cc35d21728d7afd540c943c0b4a951832600eb5bcbc23e77cc5993e88","observation_id":"36c96f89-ccae-4fd8-997c-a8dfdb27518a","resolution":{"observed_at":"2026-07-03T11:18:03.481127Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2606.19302","last_updated":"2026-06-17T17:26:22Z","snapshot_observed_at":"2026-07-06T23:54:37.596257Z","submitted_at":"2026-06-17T17:26:22Z","title":"Optimal scenario design for climate emulation","version":1},"reference_index":102,"source":"arxiv_source","source_observed_at":"2026-06-26T18:43:06.382026Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2606.19302"},"observation_digest":"sha256:83987e7fedd60419510ae77a93f661512783b78cf8d8629465b7d6ebb16395b9","observation_id":"d9aa40dc-ffef-4c6d-a6d3-ed738d7efcfa","resolution":{"observed_at":"2026-06-26T18:49:44.408657Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2606.20196","last_updated":"2026-06-30T06:11:47Z","snapshot_observed_at":"2026-08-03T23:25:05.101981Z","submitted_at":"2026-06-18T13:11:13Z","title":"Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-06-26T18:03:47.140620Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2606.20196"},"observation_digest":"sha256:d39e74d1c68f7ecbcd7eb59c2f69a2fdd7907b6a82d92fb53949e0ae048f3622","observation_id":"3b40e57a-24e3-43b4-a432-05ca2e1edb1d","resolution":{"observed_at":"2026-07-04T03:29:29.752600Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2606.20196","last_updated":"2026-06-30T06:11:47Z","snapshot_observed_at":"2026-08-03T23:25:05.101981Z","submitted_at":"2026-06-18T13:11:13Z","title":"Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-07-01T07:08:57.768207Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2606.20196"},"observation_digest":"sha256:40584bf5143685e64262cb6db54e3f834ae32104e60ff18902948cdbb354d7eb","observation_id":"e01d2ee8-3fcb-4858-9399-e83b2a9f0c19","resolution":{"observed_at":"2026-07-01T08:45:35.532946Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2606.22975","last_updated":"2026-06-22T07:55:55Z","snapshot_observed_at":"2026-07-06T23:57:47.047975Z","submitted_at":"2026-06-22T07:55:55Z","title":"TaLK: Text-attributed Graph Dataset Distillation via Coupling Language Model with Graph-Aware Kernel","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-06-26T08:53:13.162764Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2606.22975"},"observation_digest":"sha256:f0c83009bcf34b075ceaab17b6129b8c404db2f77aececa80c5cc5703d5416fe","observation_id":"2470b46e-d417-4786-8a87-ea2612f35192","resolution":{"observed_at":"2026-07-04T10:29:44.399176Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2606.29464","last_updated":"2026-06-28T15:41:31Z","snapshot_observed_at":"2026-07-07T00:03:27.045821Z","submitted_at":"2026-06-28T15:41:31Z","title":"Rank-Aware Hyperbolic Alignment for Vision-Language Dataset Distillation","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-06-30T07:21:46.599943Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2606.29464"},"observation_digest":"sha256:4079be44c7ac1d01ff451b196b689f117beda75350cd8f9fff47acfa498eb29a","observation_id":"326eee63-af80-4b80-bb3c-b0aaeb6cc625","resolution":{"observed_at":"2026-06-30T07:24:21.205476Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2606.29837","last_updated":"2026-06-29T06:24:53Z","snapshot_observed_at":"2026-07-07T00:03:45.783349Z","submitted_at":"2026-06-29T06:24:53Z","title":"Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-30T06:54:19.251168Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2606.29837"},"observation_digest":"sha256:822f20dacc4214e7b4b3b2d279c7d657b6a35c137e987465fe476b7e4982707c","observation_id":"ce914ddb-1da0-454c-b89f-71d321d2715c","resolution":{"observed_at":"2026-06-30T07:14:21.991350Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":"1811.10959","doi":"10.48550/arxiv.1811.10959","metadata_source":"pith","pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dataset Distillation","venue":"cs.LG","work_id":"e5036812-7ef1-4616-8677-c754d141d74f","year":2018},"citing_paper":{"arxiv_id":"2607.00916","last_updated":"2026-07-01T13:21:44Z","snapshot_observed_at":"2026-08-03T02:46:32.964561Z","submitted_at":"2026-07-01T13:21:44Z","title":"Condensing Large-Scale Datasets Directly with Minimal Information Loss","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-02T13:59:59.488216Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2607.00916"},"observation_digest":"sha256:a72af9d4e24bad38d763a17d28d6dbf38ec2aa6dc560e4b5b24fda06a950a7fb","observation_id":"edbd4ee1-3a92-4ee7-a588-36b5aa2b4162","resolution":{"observed_at":"2026-07-02T14:07:02.375038Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-07-12T04:56:04.713121Z","title":"Dataset distillation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.03097","last_updated":"2026-07-03T08:34:11Z","snapshot_observed_at":"2026-08-04T20:25:04.516004Z","submitted_at":"2026-07-03T08:34:11Z","title":"Heterogeneous Graph Condensation via Role-Aware Clustering","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-07-12T04:56:04.713121Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2607.03097"},"observation_digest":"sha256:9d6301f5d4513be3026de2c9bd1e8894c60ce73588224cfee458b07183c0b6c9","observation_id":"f0a00e35-1f9a-47d8-bf9e-38647670aae5","resolution":{"observed_at":"2026-07-12T04:56:04.713121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-02T04:57:33.262320Z","title":"Dataset distillation.arXiv preprint arXiv:1811.10959, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.13541","last_updated":"2026-07-15T07:44:14Z","snapshot_observed_at":"2026-08-02T04:57:28.448908Z","submitted_at":"2026-07-15T07:44:14Z","title":"When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix Training","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-02T04:57:33.262320Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2607.13541"},"observation_digest":"sha256:448ac7b4628f2bec1cb737847b6d3c12c49e6cd547573bc387e6d200120ac985","observation_id":"68a086b0-8104-4310-83f5-9a33a912fc6f","resolution":{"observed_at":"2026-08-02T04:57:33.262320Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-01T19:49:26.783420Z","title":"Dataset distillation.arXiv preprint arXiv:1811.10959, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.16859","last_updated":"2026-07-18T15:47:03Z","snapshot_observed_at":"2026-08-01T19:49:19.386440Z","submitted_at":"2026-07-18T15:47:03Z","title":"Dataset Distillation by Influence Matching","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-01T19:49:26.783420Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2607.16859"},"observation_digest":"sha256:8ba2c826cc66a2761ee3a05b24d743d96ee50e1d59bc65ea7d68663381c97f1d","observation_id":"d24a4a77-3688-4054-a729-2c4b51a5474a","resolution":{"observed_at":"2026-08-01T19:49:26.783420Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-01T16:12:21.433340Z","title":"arXiv preprint arXiv:1811.10959 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19426","last_updated":"2026-08-04T08:29:29Z","snapshot_observed_at":"2026-08-05T22:25:12.793688Z","submitted_at":"2026-07-20T15:46:18Z","title":"Making Single-Cell Data Distillation Auditable: Traceable Real-Cell Coresets via Discrete Min--Max Selection","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-01T16:12:21.433340Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2607.19426"},"observation_digest":"sha256:b3312db4d40e6c551b919bb8989e1b76dfd1471c5a634bd329f30e9461a25000","observation_id":"4d6e7a17-8a35-46c1-82b2-fe1b6aba98d9","resolution":{"observed_at":"2026-08-01T16:12:21.433340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-02T07:40:23.785282Z","title":"arXiv preprint arXiv:1811.10959 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20532","last_updated":"2026-07-10T13:07:12Z","snapshot_observed_at":"2026-08-04T10:36:11.852500Z","submitted_at":"2026-07-10T13:07:12Z","title":"Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development","version":1},"reference_index":108,"source":"arxiv_source","source_observed_at":"2026-08-02T07:40:23.785282Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2607.20532"},"observation_digest":"sha256:fbab272f1e653122ad1dd69da9900adcd29124109b04c83f65c628282d378fac","observation_id":"5211b8a4-d4e2-4068-b8c1-3e1476831780","resolution":{"observed_at":"2026-08-02T07:40:23.785282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-01T02:50:16.957778Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.25318","last_updated":"2026-07-31T07:43:16Z","snapshot_observed_at":"2026-08-05T22:10:27.177664Z","submitted_at":"2026-07-28T06:05:12Z","title":"Dataset Distillation Based on Saliency-Driven Prototype Alignment","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T02:50:16.957778Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2607.25318"},"observation_digest":"sha256:b6b4dcafa6c2dbf46db98421115494c7a90bd33ae36c39bd7382f308f795a2fe","observation_id":"5de0bfd9-0118-43cf-9957-8c6ef85a8b00","resolution":{"observed_at":"2026-08-01T02:50:16.957778Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-03T01:50:43.747255Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.25318","last_updated":"2026-07-31T07:43:16Z","snapshot_observed_at":"2026-08-05T22:10:27.177664Z","submitted_at":"2026-07-28T06:05:12Z","title":"Dataset Distillation Based on Saliency-Driven Prototype Alignment","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T01:50:43.747255Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2607.25318"},"observation_digest":"sha256:007f420ae67038ff9201acaa5c3657c804d5052aaa2f77e8d1ee337fc4257dc7","observation_id":"379cf9c8-7172-4196-8275-dc22ef990d72","resolution":{"observed_at":"2026-08-03T01:50:43.747255Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-01T12:02:45.593047Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.26628","last_updated":"2026-07-29T08:54:45Z","snapshot_observed_at":"2026-08-01T23:27:58.161626Z","submitted_at":"2026-07-29T08:54:45Z","title":"Understanding Context Sampling in TabPFN on Small Tabular Datasets","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T12:02:45.593047Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2607.26628"},"observation_digest":"sha256:c322528e6a8899b766bb9ab841ba6241ec61e209f6b139437fb5f341db5c25f1","observation_id":"720bfe6c-8911-429a-974d-6392ffa46717","resolution":{"observed_at":"2026-08-01T12:02:45.593047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-07-30T21:50:18.172564Z","title":"arXiv preprint arXiv:1811.10959 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26763","last_updated":"2026-07-29T11:04:01Z","snapshot_observed_at":"2026-08-04T18:12:24.793106Z","submitted_at":"2026-07-29T11:04:01Z","title":"Long-Tailed 3D Point Cloud Dataset Distillation","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-07-30T21:50:18.172564Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2607.26763"},"observation_digest":"sha256:24a13727b4fa73cad7ae3bba8bf4f896d58753e11319080ba82fb10385c3854d","observation_id":"5328e029-a548-4cdc-8e33-ceb2a6515223","resolution":{"observed_at":"2026-07-30T21:50:18.172564Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10959","snapshot_observed_at":"2026-08-05T22:10:41.599390Z","title":"arXiv preprint arXiv:1811.10959 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.03269","last_updated":"2026-08-04T07:46:28Z","snapshot_observed_at":"2026-08-05T22:26:11.250507Z","submitted_at":"2026-08-04T07:46:28Z","title":"Efficient Video Dataset Distillation via Cluster-Guided Prototype Blending","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-05T22:10:41.599390Z"},"links":{"cited_paper":"/paper/1811.10959","citing_paper":"/paper/2608.03269"},"observation_digest":"sha256:ca10380c031071bb54be854f1534466c6af316089df39a9d2969751fed5cb1bc","observation_id":"75e79b80-c378-4033-90d8-1cf81b9ea762","resolution":{"observed_at":"2026-08-05T22:10:41.599390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1811.10959/citation-record","integrity":"/paper/1811.10959/integrity","json":"/paper/1811.10959/citation-record.json","paper":"/paper/1811.10959"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"University of Montreal , volume=","venue":null,"work_id":"1d0f227a-98a7-4882-9337-62d6901326b0","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:c2ae003b310a324b3df3df596fce4eccd9c1e045f5bd8d74113f47a8db77929a","observation_id":"7a0bbd90-052a-49cd-bf8f-65234617c7df","resolution":{"observed_at":"2026-05-23T23:53:20.468067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the fourteenth international conference on artificial intelligence and statistics , pages=","venue":null,"work_id":"08387c87-da27-4b8d-86ca-aae54314deb3","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:57415f313ece81dfafc0ea135a129e566dc34f8be5b3d27b2edbc254172e9cb1","observation_id":"8be75599-48da-44c3-9ac1-023d316c7dc2","resolution":{"observed_at":"2026-05-23T23:53:20.475159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2014 , organization=","venue":null,"work_id":"9b285a2c-5aa6-4b4b-b822-3429921a479a","year":2014},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:c2303c39c761c073f8ef0a43b08bf2ce0da5a4fcf6667d0ef531801f17cd1d1a","observation_id":"f9215bf6-cd7a-4bf5-af81-c509117549cd","resolution":{"observed_at":"2026-05-23T23:53:20.478417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advances in neural information processing systems , pages=","venue":null,"work_id":"ab12cb76-187c-4707-a846-c415c0f71048","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:6ceece4e2c7c332ce901c66a06a047109ebfe985328442fa3435d237644c820c","observation_id":"3b6d5b80-a304-4163-906d-72f505091730","resolution":{"observed_at":"2026-05-23T23:53:20.481880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"9135105b-a169-43ea-99a7-7402756f9977","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:7b930b104099a231538297abbaf0e1c52d89a6230df6a27b2ce49b3d8929a08b","observation_id":"1143d780-81dd-4ab7-a203-f2eb95f06474","resolution":{"observed_at":"2026-05-23T23:53:20.485146Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"e66fd21f-63aa-4be7-8d3a-9589c0a937db","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:1d6339b68b7538fe9ad6601cc4cdf1608494551d77b89af4446a98da15526434","observation_id":"bd822a08-043b-4476-8983-c2892bbdafbb","resolution":{"observed_at":"2026-05-23T23:53:20.488409Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advances in Neural Information Processing Systems , pages=","venue":null,"work_id":"b682a878-1f14-4b84-bb96-123ef0ef8fba","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:aa26f3b54d6cef7e1dbb2fb2a75abcad653f8986b690d2ffe62a3bb120e94416","observation_id":"832ef9bf-99c2-4a16-b558-2d1432262f77","resolution":{"observed_at":"2026-05-23T23:53:20.491655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T11:16:11.879380Z","title":null,"venue":null,"work_id":"1654b63f-3609-43a2-9e4c-10e00069e7aa","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:d3c92e9a899977aac0c414136c01592d35dbacd2fcf0c6c0416bf1bdaecbc5c3","observation_id":"dd04d3cb-571a-481b-9fd7-99463bc4fce1","resolution":{"observed_at":"2026-05-23T23:53:20.500350Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"b14b1c04-f25d-4f43-a0ab-ebec16900e72","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:8684d8c71aec4e8f64c590d9049203c572e4feb3f1a764130e5d080bff65b8ee","observation_id":"d2aa77b7-fa51-4caf-9a91-4187ebcc9089","resolution":{"observed_at":"2026-05-23T23:53:20.504799Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Technometrics , volume=","venue":null,"work_id":"bb6f4efa-c668-48f2-871a-53af22128dc6","year":1980},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:a0af547a4313854d4be9622d57cc30a67356387d115068a6b469f367e9ffa47f","observation_id":"e64b0a65-27f1-4dc7-a3bf-f75d2a3b7e68","resolution":{"observed_at":"2026-05-23T23:53:19.838091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2011 , organization=","venue":null,"work_id":"a14ccef6-a4bb-4040-90c6-d6dbc03d9437","year":2011},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:fe2d8b7758cedcfc66a077b5c3a483a3b3b446c1dd7d808ee720161c3b398ee5","observation_id":"1c1f0ade-a7c8-4e8e-b41d-552bca52ac9a","resolution":{"observed_at":"2026-05-23T23:53:19.842866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Toward category-level object recognition , pages=","venue":null,"work_id":"2d4d23c0-4bf5-47b9-b314-2a4a93710350","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:df8dee87d8c0517a98cdfccbac624c0dccdc60500f15b3f067296564b09175e0","observation_id":"61599368-b3da-4e94-b233-4f0375caf2ce","resolution":{"observed_at":"2026-05-23T23:53:19.851583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"11a59711-cec3-4c0c-8ba7-06f32eea7a1d","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:1fb592d2eb86b5af1e7bfd312438b489c8de2d9d34a9743e2e5bdb98553a2d1a","observation_id":"43d3b2a6-eef8-48e3-a52e-d512e14d1e46","resolution":{"observed_at":"2026-05-23T23:53:19.855775Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"be835ec9-ecb0-4fc6-ab07-db4b25455caf","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:a0fae4c1f7c8a56c83463085e57ab3074414faf880e6d0fdf35e052b8112681f","observation_id":"7400f3ce-5170-420f-a349-1227adcf1a0d","resolution":{"observed_at":"2026-05-23T23:53:19.862206Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"ICLR Workshop , year=","venue":null,"work_id":"e8c0c03a-8537-4597-8c61-bfa544ad3356","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:31cc63b0024f1dd972b68e8ef60446fee17005168e176e03ab20166f1872b613","observation_id":"02b18ffb-50aa-4671-b4a9-c64b17632156","resolution":{"observed_at":"2026-05-23T23:53:19.866816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"8f844106-9fdb-4210-a074-a8facf75daac","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:47d1f3183823823612cec42cabaf8c811d0548f8517e381905884d344f6e6e81","observation_id":"e7c7d5e5-3b68-4488-8d3e-fc3e597096fd","resolution":{"observed_at":"2026-05-23T23:53:19.870483Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4ac4196a-9e76-457d-878d-444250e503aa","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:d44b304a990399f1fb1c2871fdb247479e0d19af2ff789a5d829e4a851711738","observation_id":"dafcb136-a3d8-496d-a1f4-d7fcc4293558","resolution":{"observed_at":"2026-05-23T23:53:19.874166Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2010 , publisher=","venue":null,"work_id":"ce3f76f9-dc8e-401a-bd00-2798c7d036cd","year":2010},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:99ecd466d36749509cac28b0869ab32b5cc573e01c92f2c8e3e366b4212f0288","observation_id":"da3e682e-a1b5-4905-a68f-01e75496b82c","resolution":{"observed_at":"2026-05-23T23:53:19.880230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":"4e11da7b-5ade-4236-8f2d-6ee515a07323","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:86afc9e2fa7b86f0484c0ce12e4efd108b2a770ee5b9b858a73ee88a0cbb8cd2","observation_id":"4d7eab98-f2a9-4c86-a5e4-5b2efb1f60f8","resolution":{"observed_at":"2026-05-23T23:53:19.884592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"d249306e-7920-42b1-aea7-7220d9efe6a2","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:648436d5cfe3031d5c806bdd847daf71b98fa691b57377aeee467f048accbec1","observation_id":"2ad6f87a-dcdb-4d55-bcb3-fcf89bac8603","resolution":{"observed_at":"2026-05-23T23:53:19.888904Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Journal of artificial intelligence research , volume=","venue":null,"work_id":"61e5941a-e3fc-40b6-9488-65d11f1d313a","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:bf87885ed81b110894688612dccc2f72d4b4a2a0d86470229fe26c11635f14f3","observation_id":"537144a6-13af-45d0-aec7-b069a726dfa2","resolution":{"observed_at":"2026-05-23T23:53:19.893976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T22:47:38.240021Z","title":"Proceedings of the IEEE , volume=","venue":null,"work_id":"baf04aeb-b0ea-44ab-83c8-5ba388861570","year":1998},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:1a5f66915acd1e8d371466ca69ed65a84c638f73345e1dcfd836773659a5d72e","observation_id":"6a0201c7-06be-476f-a080-03a35a8dd084","resolution":{"observed_at":"2026-05-23T23:53:19.903572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"International conference on artificial intelligence and statistics , year=","venue":null,"work_id":"544e9a84-21ff-438b-b58f-a94a1f3ed87c","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:f598ed53ad3e8d0e0281f0bb802d3ed0f4987ad6e05308e5fb9cd086e1ce0784","observation_id":"64113136-36aa-4ba2-8cea-bc15a72e34ac","resolution":{"observed_at":"2026-05-23T23:53:20.034989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4869a50c-93f7-491c-bc4c-43986b1dd06e","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:e4e5eb66356b33f2e008e9e81a42b5bf2b2febd8c352835d49365f6958b8070f","observation_id":"bc56e276-b52c-46f8-bda8-99b3fbe5f4cc","resolution":{"observed_at":"2026-05-23T23:53:20.045832Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2015 , booktitle =","venue":null,"work_id":"29b650e7-4f5b-4bb5-ad41-18e075d2c62b","year":2015},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:4efff0f00598b85223d5e80e92588a7da6e17ce123fc252ff91344ea823479ac","observation_id":"7caf4d2e-d3c4-459b-a191-ba61b88f0b77","resolution":{"observed_at":"2026-05-23T23:53:20.052588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"author=","venue":null,"work_id":"5e1da383-b3b1-41fc-a71e-ca03dc7e32e7","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:0e4310dc37503cad2f2a4d068fc3efc46f44d4109889dcf6f8461bbe5775fa93","observation_id":"0c55ae94-ca53-4ad7-a086-5fed24a01c87","resolution":{"observed_at":"2026-05-23T23:53:20.059156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4b7d1bb7-49d6-4be1-bcad-90826206e4f6","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:62c431dd422152bd639d5a9e74ad505bda5d41ebbff5c0b171d5bbe6d1c9594d","observation_id":"955e925b-e2ad-4802-9e4a-8bcf0ab6a764","resolution":{"observed_at":"2026-05-23T23:53:20.063175Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6572","last_updated":"2015-03-20T20:19:16Z","snapshot_observed_at":"2026-07-06T04:04:16.777653Z","submitted_at":"2014-12-20T01:17:12Z","title":"Explaining and Harnessing Adversarial Examples","version":3},"cited_work":{"arxiv_id":"1412.6572","doi":"10.48550/arxiv.1412.6572","metadata_source":"pith","pith_arxiv_id":"1412.6572","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Explaining and Harnessing Adversarial Examples","venue":"stat.ML","work_id":"2cedf8f6-7539-4c49-8136-f42a20487146","year":2014},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"cited_paper":"/paper/1412.6572","citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:0ada5948b6648e20c7f55344cf0c350a9de3746dab573890e012dca9054e8d4a","observation_id":"2952f54c-8a52-4e49-a5c1-4441c2f3e6ad","resolution":{"observed_at":"2026-05-23T23:53:19.774434Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6199","last_updated":"2014-02-19T16:33:14Z","snapshot_observed_at":"2026-07-06T03:31:33.797310Z","submitted_at":"2013-12-21T03:36:08Z","title":"Intriguing properties of neural networks","version":4},"cited_work":{"arxiv_id":"1312.6199","doi":"10.48550/arxiv.1312.6199","metadata_source":"pith","pith_arxiv_id":"1312.6199","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Intriguing properties of neural networks","venue":"cs.CV","work_id":"7bcd9f41-780c-4b4b-9a08-830d4177cdd8","year":2013},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"cited_paper":"/paper/1312.6199","citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:1c6441840389544f77d6d54ee79f0b345b8e622a5233cd7cf99cd3f14f71565f","observation_id":"de95c7c4-dd0f-46e8-b099-344be8dcd117","resolution":{"observed_at":"2026-05-23T23:53:19.816678Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security , pages=","venue":null,"work_id":"d0efacd5-287a-4bf0-9b19-b7236c03f1d8","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:2680ed831f1f0c5431ee32fec34df37958dcdf5631df99268e93605a2f8daa27","observation_id":"f7fe81c0-80f3-4240-8543-fa49544ebf0c","resolution":{"observed_at":"2026-05-23T23:53:20.067041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.05526","last_updated":"2017-12-15T04:26:26Z","snapshot_observed_at":"2026-07-06T06:14:30.795326Z","submitted_at":"2017-12-15T04:26:26Z","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","version":1},"cited_work":{"arxiv_id":"1712.05526","doi":"10.48550/arxiv.1712.05526","metadata_source":"pith","pith_arxiv_id":"1712.05526","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","venue":"cs.CR","work_id":"bb1fb326-f0f6-4c72-a4d2-eb7f0707b971","year":2017},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"cited_paper":"/paper/1712.05526","citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:4e3a20b995c96d79fb4fc7fec09e4cc8fee5588067fc25a35406fa32cf141bbf","observation_id":"2a1fa22d-35a8-48cb-addc-26cf2ccfcddd","resolution":{"observed_at":"2026-05-23T23:53:19.797924Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6806","last_updated":"2015-04-13T07:58:17Z","snapshot_observed_at":"2026-07-06T04:04:22.105016Z","submitted_at":"2014-12-21T16:16:37Z","title":"Striving for Simplicity: The All Convolutional Net","version":3},"cited_work":{"arxiv_id":"1412.6806","doi":null,"metadata_source":"pith","pith_arxiv_id":"1412.6806","snapshot_observed_at":"2026-07-04T05:59:36.928402Z","title":"Striving for Simplicity: The All Convolutional Net","venue":"cs.LG","work_id":"31127b64-cba9-4eeb-8256-888751a69827","year":2014},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"cited_paper":"/paper/1412.6806","citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:ba9fe8523b558eb514d74e5f62f86bd770a22a6e89b34e29553a0cd4fedd0f70","observation_id":"b13542ca-31ce-405a-afb3-18a848a85b87","resolution":{"observed_at":"2026-05-23T23:53:19.804624Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"dc1cf9d0-bfa5-4cda-ad7f-76ad53e4cdb8","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:ff538d821b49706a1d0c9b79df9fb493b974e4e2ac6507fdc3335527a202f84f","observation_id":"2bf5d3b6-e98a-4388-ab46-004d27182c50","resolution":{"observed_at":"2026-05-23T23:53:20.071186Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a7f90244-ee99-48b6-a0b9-3c7b9713541a","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:f8b6db8374a752aa808acc28390641c591c0e7b05511c7261b3da6a6c7b65be3","observation_id":"18e5c24b-ef12-4fde-9ab6-e18e95ed6911","resolution":{"observed_at":"2026-05-23T23:53:20.074850Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"759b6201-0bb7-4ca2-b54b-feacd36036ae","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:eb3d26c39833062d35701e460785f917cfd9d19ffec72e7f9d43afcc8b94e58b","observation_id":"210e296a-3284-4f32-9343-65c49cd75532","resolution":{"observed_at":"2026-05-23T23:53:20.078947Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"5f1467fc-ca7f-48d7-a2a4-4bb38c318494","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:4f364b1cd99252cf3bed5e6e7fce51e65e52e51c87d8b475d77cf7bd5c6928cd","observation_id":"ff55cd6a-4fb8-4bc1-8b70-8122230d1cb3","resolution":{"observed_at":"2026-05-23T23:53:20.082504Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3ca72068-488d-4a3d-9cca-bce51becc09e","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:80fc0fa91a6f85ae8f40ab44515beb9f0d7577cfeb9b26fbe0be1586eee5c0ac","observation_id":"0cd660c9-ce01-47a5-94cd-5aa894f383f1","resolution":{"observed_at":"2026-05-23T23:53:20.086528Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"40ce5e78-1184-4c9d-b15e-de403195738e","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:47aec8aafacf4f343ed48873f2c3e74c668928fd97fe84bd6f32099b8bf7b20b","observation_id":"76d7a5a8-ad08-4c2c-8389-8187478e8423","resolution":{"observed_at":"2026-05-23T23:53:20.094761Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neural computation , volume=","venue":null,"work_id":"485e09c5-ce3f-4b4f-bea4-c6f4ea024d7d","year":2000},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:a2e6cac66642ab06ebd167585678af6cf0a4b156b783f88866e3f66b53ac6c00","observation_id":"02bcc8df-8da9-4d3c-97d9-8134ed801d62","resolution":{"observed_at":"2026-05-23T23:53:20.098903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Artificial Intelligence and Statistics , pages=","venue":null,"work_id":"397c7d96-909f-41a9-8b90-9b2db0e9110d","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:26c1c68be2b2d79d0b362d2b524c3489783ab3714fdacc30c5df8448866e661b","observation_id":"52c932c0-f96f-4971-b58f-1ddc08e743cf","resolution":{"observed_at":"2026-05-23T23:53:20.102637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"d171773c-6800-4c18-ac85-9d789a4ef8c1","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:77b03b876656259241982cea9b6938fee882568dce101c11ca48c1573bd3aaa6","observation_id":"77c70f28-2625-4a0c-85f6-bf6776fdc529","resolution":{"observed_at":"2026-05-23T23:53:20.106423Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Zisserman, A","venue":null,"work_id":"38c5d8ad-77c4-4e66-876a-403fe6f5d8b8","year":2014},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:4bbbe246530191b1b2f44e48bdc0760d11bf9f39e07cb8031af35958fe5a27dd","observation_id":"195f07d7-909f-41a3-a486-d3c4b4f99255","resolution":{"observed_at":"2026-05-23T23:53:20.110617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T14:46:18.262067Z","title":null,"venue":null,"work_id":"9d560ccf-7f3a-4f3e-bf09-d937880939ac","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:0fd7d326bac9dcf1d6e3ce7ae3b7bd894df237d7c280b27eb3d6172d0cb6496f","observation_id":"52484db0-c344-4e56-8257-96219b7a53ba","resolution":{"observed_at":"2026-05-23T23:53:20.115271Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-07T07:33:30.933604Z","title":"Neural computation , volume=","venue":null,"work_id":"8084af53-b64a-4fad-a54c-849d0481feee","year":1994},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:a835b750b0acce7362b603c54c00691bd2ca48a80383f2b642a31f11b3db2430","observation_id":"266a7e0c-36dc-4f8d-86c6-0d5f782c454b","resolution":{"observed_at":"2026-05-23T23:53:20.119276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-06T17:52:44.739807Z","title":null,"venue":null,"work_id":"4b0af8a8-90de-44b7-825a-264326f456f3","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:86d77e85d0b11f0d9c8279a4afac23d611e7236672a3bd9b5e502a4e6c7c4977","observation_id":"c9d44e43-19c2-462d-b154-083d4d8df985","resolution":{"observed_at":"2026-05-23T23:53:20.123094Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2009 , pages=","venue":null,"work_id":"c367d714-a2f3-48ec-9921-83614574f6e1","year":2009},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:80506a31395f2246948af72faf7351bab298df33ac377f527dfe75289e00b664","observation_id":"dedaa2d4-e5d9-4a61-b565-af6d33f39adf","resolution":{"observed_at":"2026-05-23T23:53:20.126672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06856","last_updated":"2016-09-22T22:14:17Z","snapshot_observed_at":"2026-07-06T04:37:20.777239Z","submitted_at":"2015-11-21T09:07:08Z","title":"Data-dependent Initializations of Convolutional Neural Networks","version":3},"cited_work":{"arxiv_id":"1511.06856","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.06856","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Data-dependent Initializations of Convolutional Neural Networks","venue":"cs.CV","work_id":"4062f9be-a2de-4f12-82fd-a3faa2d8af14","year":2015},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"cited_paper":"/paper/1511.06856","citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:d60f15dcf3ba7d3465d8c406dff3523d2d5efd035a57bd701edc93d3cbdbc50c","observation_id":"6d37b845-90c4-43c6-9cf4-ac761ca8b7f9","resolution":{"observed_at":"2026-05-23T23:53:19.783221Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T03:47:54.372474Z","title":"Proceedings of the IEEE conference on computer vision and pattern recognition , pages=","venue":null,"work_id":"da360c40-6481-4088-bd96-8e73e0280a6b","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:66ff9ea8836f8433fa39f6040ad758d581b15acbf3e367e904e3c473c7d41cb7","observation_id":"9cb48ac9-7005-449b-b6c4-6c02b8cbef63","resolution":{"observed_at":"2026-05-23T23:53:20.132906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T04:20:40.419293Z","title":"http://yann","venue":null,"work_id":"91febc96-cf80-4f87-a327-c93e3f303ee2","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:99bd260d1b2112d739f1335df4e30b9d53f8ba1f20ade50f0173435bec5b065f","observation_id":"e3c68831-c8a0-4057-b37f-7944bf208d97","resolution":{"observed_at":"2026-05-23T23:53:20.137160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T02:17:56.405773Z","title":"2009 , institution=","venue":null,"work_id":"4ff8cee6-a426-4ffa-969b-d80bf248b33c","year":2009},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:aad979bf7325fca378b55b0fb42b0566e1819d1c24672b43d7b35c3ee9b9ab2e","observation_id":"461bb389-f5e2-43e4-89f3-011704fea3d2","resolution":{"observed_at":"2026-05-23T23:53:20.141806Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"https://github.com/akrizhevsky/cuda-convnet2 , year=","venue":null,"work_id":"4ab3518c-5560-4155-ac47-62d5b4ae6a9d","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:0fd03bcc4df9deef1bf1b3c6e4532b7beeda09eef3915d7296695dc0b62b94b9","observation_id":"3da2c658-491d-4d11-937e-b2b3191c6dfe","resolution":{"observed_at":"2026-05-23T23:53:20.147396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T00:10:26.365309Z","title":null,"venue":null,"work_id":"e676d6bd-b372-44a6-9472-7cc2db328693","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:ce1a1a1b2e40ba7d49fd9178bfee9083ea9d7e1c3e7ecaaee8232545f7431877","observation_id":"6eddc3e8-3741-47f8-b223-5975595dd326","resolution":{"observed_at":"2026-05-23T23:53:20.151303Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"73e4adf7-757f-41cb-88c8-acada76b1753","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:234b33fde908dbc1aae64bbb07c49c003256dc9484c8958c0c4881796e28e874","observation_id":"17b568f5-9aeb-46ed-8310-692e95fb04cd","resolution":{"observed_at":"2026-05-23T23:53:20.155137Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2010 , publisher=","venue":null,"work_id":"927532fe-c7e4-4ca5-9c4f-87368b4945fd","year":2010},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:c20a9453663e90fe9581eb69f1256a9ee73b872c83c28e9aa39b826f96907da5","observation_id":"5a22d40e-1529-4b44-a922-d0acb265ed86","resolution":{"observed_at":"2026-05-23T23:53:20.158840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Branson, S","venue":null,"work_id":"a95af64d-7db6-40e1-9798-fb8c027f135f","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:45f491c9b6093652cbaba644d420212106f276cfb72077a0f853f40ef87c85fd","observation_id":"5a115e77-1602-43e2-8c68-718725e7dcf8","resolution":{"observed_at":"2026-05-23T23:53:20.162207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1404.5997","last_updated":"2014-04-26T23:10:51Z","snapshot_observed_at":"2026-08-03T21:57:53.090760Z","submitted_at":"2014-04-23T22:37:56Z","title":"One weird trick for parallelizing convolutional neural networks","version":2},"cited_work":{"arxiv_id":"1404.5997","doi":null,"metadata_source":"pith","pith_arxiv_id":"1404.5997","snapshot_observed_at":"2026-06-29T13:33:27.609259Z","title":"One weird trick for parallelizing convolutional neural networks","venue":"cs.NE","work_id":"ad32a219-5e6c-4d6b-999b-487749b8c9c3","year":2014},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"cited_paper":"/paper/1404.5997","citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:0a24059c2808eb914e854f1251a60efce464e867faa6b329b072a7cff1268e69","observation_id":"dd1085f2-a9a4-46b2-826b-ff54dbbce459","resolution":{"observed_at":"2026-05-23T23:53:19.832763Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"01e5b610-cd33-4c4a-a91a-ffcde20e9913","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:818429c34077bcdf9caea685a45f4b44380969336bf3e3059f8bfc3f9dcf407b","observation_id":"0b6eff52-97bb-45ae-bbc3-cfb0c17eac9e","resolution":{"observed_at":"2026-05-23T23:53:20.172916Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"36079e37-f7eb-4b3b-859e-3ba3e14d128c","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:06d3c4ef7eada4a1ea39b49c955f1baa4652a4ae6ee24554946f255850ac2d72","observation_id":"2d202dde-8527-4d90-af28-e3991ac19346","resolution":{"observed_at":"2026-05-23T23:53:20.176704Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Discrete & Computational Geometry , volume=","venue":null,"work_id":"a4657bea-b5a7-4580-97ee-1e02f33ee45f","year":2007},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:81cef5acf584e83db9f781bbb98af37d7b961355f53fd95a8ded570ec5bcd3b9","observation_id":"05e9d1f6-38ed-48a4-b945-0a4c65d1bb35","resolution":{"observed_at":"2026-05-23T23:53:20.180857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Artificial Intelligence Review , volume=","venue":null,"work_id":"a6270b49-c4bc-47cc-9bfd-72373019f334","year":2010},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:1bb7de8e501002d8956606f672257fe7c447ed623264ece6ad9154c3a0d1b644","observation_id":"6e9c18d5-7f3b-421a-a359-ee1ff5d0456c","resolution":{"observed_at":"2026-05-23T23:53:20.184455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"50b6fc92-a4b9-4edd-8786-a7c3d947f4c4","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:3074466fbf49f32e5ef1b4134e1b7203f60e252e62c2b131ee12b5103517ccf3","observation_id":"9ac2c8ad-c75f-4571-91ec-9288bfbdc2f4","resolution":{"observed_at":"2026-05-23T23:53:20.187796Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"NIPS workshop , year=","venue":null,"work_id":"474d41f4-94d7-40ff-aef7-7f9e6d845d6d","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:77e67a90729d3c1911f8a76c743b7300311ae5902f071ec17e789a6c060ab55b","observation_id":"0f675e0c-e80f-44e5-ab65-99a766c1cac2","resolution":{"observed_at":"2026-05-23T23:53:20.190814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"5b7efebf-1b3f-45a7-98f6-fe5e6056b22c","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:c89c0d8da947481b1ea3d401ce0927e9998ddd4f262c1ebc82085f84890d8489","observation_id":"b76e3327-574e-4e7e-9d7e-98d7f9d1b8d5","resolution":{"observed_at":"2026-05-23T23:53:20.194152Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"1957 , publisher=","venue":null,"work_id":"7dffee0f-8aa2-4c92-9ff5-0c3dde4bfaf7","year":1957},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:ff1e740283b6d7f024e7f0fb40f5207c6838e440275db0ed620eef1639e57ab0","observation_id":"d9d3a614-767c-49c6-825a-d9ee266075aa","resolution":{"observed_at":"2026-05-23T23:53:20.199073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"IEEE Intelligent Systems and their applications , volume=","venue":null,"work_id":"9ae8897d-b1c1-4723-a212-4d0a953e7554","year":1998},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:7e014529e9bdb15e7ca757659e048f93133951e798279cf8864efede5c4b028a","observation_id":"2cb4688b-a2dd-4780-bb20-d9c17161809a","resolution":{"observed_at":"2026-05-23T23:53:20.203256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Journal of Computer and System Sciences , volume=","venue":null,"work_id":"010fe56c-21f3-43a0-89a5-7fa3dead316d","year":1995},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:322987d9fb59e587bd75f0bf8a6cb268db4d691d1662b6034ae7f72d11aa81d8","observation_id":"fe52ecac-5e23-4354-a527-09080ad3e3d3","resolution":{"observed_at":"2026-05-23T23:53:20.208178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"New Generation Computing , volume=","venue":null,"work_id":"af7e5821-e5f9-492b-91a9-b1faba108863","year":1991},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:f5217acca88013be432c488e9c87922e4adf13c9766380c94f1b0192ff9e91ad","observation_id":"cbf5cf2f-4a66-424b-bc0f-55c066013255","resolution":{"observed_at":"2026-05-23T23:53:20.212350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a47d4835-fbd9-49a7-be3b-7fe044b19bea","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:c8874c1386db2cb173086dc2c0b0cc85e46348fd7c737912a21f92a1d408f814","observation_id":"417a22d9-5678-4bc2-ac90-c1b18947557d","resolution":{"observed_at":"2026-05-23T23:53:20.216607Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"author=","venue":null,"work_id":"3a2cfe01-1cd4-41b1-abb8-7cf557418abd","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:b3dc4bca8282e82601a66670fbabc49092bd13a6671cdfe292f9c0072cbc7087","observation_id":"4753658f-428a-4e3f-b48f-d1fcb32c0af4","resolution":{"observed_at":"2026-05-23T23:53:20.223624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T05:20:42.801188Z","title":null,"venue":null,"work_id":"19a9a385-51d7-461d-b64c-20849397d2e4","year":null},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:a5d5b0d5de1975589a1755a865a4fae46ea16278c2cb4b0ffe21672546b4db6d","observation_id":"91664acc-aed1-4d41-811e-3faed703402d","resolution":{"observed_at":"2026-05-23T23:53:20.229090Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Pruning training sets for learning of object categories","venue":null,"work_id":"12003fe0-8002-4f7e-89e2-04c2792111bc","year":2005},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:ee157b5061a7e54bf6f0a9590f96df715960736d9272841112c3a3ae32c86924","observation_id":"c004e1fc-78e3-46bb-9c98-8eba0d0e6da6","resolution":{"observed_at":"2026-05-23T23:53:20.241722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Do deep nets really need to be deep? In NIPS","venue":null,"work_id":"4de96eef-0d67-4988-b075-83be00eb2400","year":2014},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:8ba178eaff754c98d5501df8035b05cb0d7369346ee70da2146979001100a2f2","observation_id":"34d1b1d5-545b-4260-86aa-24ab8b4c32bf","resolution":{"observed_at":"2026-05-23T23:53:20.247720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1703.06476","last_updated":"2017-06-04T22:40:16Z","snapshot_observed_at":"2026-07-31T23:59:25.672418Z","submitted_at":"2017-03-19T17:45:29Z","title":"Practical Coreset Constructions for Machine Learning","version":2},"cited_work":{"arxiv_id":"1703.06476","doi":null,"metadata_source":"pith","pith_arxiv_id":"1703.06476","snapshot_observed_at":"2026-07-02T06:16:43.730304Z","title":"Practical Coreset Constructions for Machine Learning","venue":"stat.ML","work_id":"0187d51b-c318-439d-83b3-ae9c13adf037","year":2017},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"cited_paper":"/paper/1703.06476","citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:978d96cb4709fc07f64b49736e4a36b378c60bfe8c2d0edf6eb5450af01b55cb","observation_id":"50267bb3-7e7a-4874-a240-d30c4d1d49e1","resolution":{"observed_at":"2026-05-23T23:53:19.810748Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Network dissection: Quantifying interpretability of deep visual representations","venue":null,"work_id":"e6239682-dfbe-451f-b921-8f57080adf02","year":2017},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:cded145a04f80a3fd7dd3cf391015716ebae368ebef0d09f2555c8161ea23207","observation_id":"b1dc8dd7-a25a-4553-9296-db380cb7de28","resolution":{"observed_at":"2026-05-23T23:53:20.252214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Gradient-based optimization of hyperparameters","venue":null,"work_id":"dc5fea9f-828f-4dfc-9dd9-11c58d2c20ef","year":1900},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:9a2395eedc8d58631d3f4d2d98d28a18ade5d8720390c2e8054d1dd2e0de6d6e","observation_id":"fbe2069d-dfab-4071-935d-59ad776081e9","resolution":{"observed_at":"2026-05-23T23:53:20.258916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Poisoning attacks against support vector machines","venue":null,"work_id":"6c61b5a3-8759-44e2-98bd-c7c8410200ca","year":2012},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:4f27d9b4421933e37c6c866915e7fb12c03c91e8c56a4229dc3cdafd9e877894","observation_id":"e1397973-5931-426c-984a-0dfdd74c977c","resolution":{"observed_at":"2026-05-23T23:53:20.263566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Active learning with statistical models","venue":null,"work_id":"8a16dd16-78ee-4d90-b9d9-b75a30494e71","year":1996},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:07e0299530c5852a04805e05ef79cffe4aa06b7cf5071c1de269372e34fe29a3","observation_id":"22c523eb-68ef-435c-be20-43015a040aff","resolution":{"observed_at":"2026-05-23T23:53:20.270758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Frustratingly easy domain adaptation","venue":null,"work_id":"b7802bca-d09c-44e5-a41b-707de261ec45","year":2007},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:430e975396c64a94e83d4945ce345698dc54b403ade09650168fc6bc8a06d5db","observation_id":"030bb08d-e2fa-47b4-bdf3-6cd3c8db2741","resolution":{"observed_at":"2026-05-23T23:53:20.274947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":"5e7fc405-4d55-4d75-9bd4-209879bdbec7","year":2009},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:5a26a62b97d60409c50c164823c39b01931997bad7c51daaa3089a8bf6cf9147","observation_id":"30c4a007-de5d-49f2-9911-a36500450fbe","resolution":{"observed_at":"2026-05-23T23:53:20.282398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Generic methods for optimization-based modeling","venue":null,"work_id":"7fd4a487-61af-4b70-8c3f-70ff4297bea1","year":2012},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:0d1cf22b0ed0c8b32970ec141d7a5e0a22ecb19e8e411ddb118b6be53a7ae084","observation_id":"780b1bc0-0623-482d-a73a-50a475404e31","resolution":{"observed_at":"2026-05-23T23:53:20.287133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The pascal visual object classes (voc) challenge","venue":null,"work_id":"906c1187-6518-414e-99d6-4540c5a9d85d","year":2010},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:e42260b0df4c6e9af04e3179d53c1b9262200edc0c6f4870379b5ea85a97c6d4","observation_id":"34c77196-0a1c-40a0-a191-985c2ba87c09","resolution":{"observed_at":"2026-05-23T23:53:20.292352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Object detection with discriminatively trained part-based models","venue":null,"work_id":"b415f001-913e-4e6b-a635-7d9ddc7193ab","year":2010},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:c327df34df6e284063a455aeb0c75721001a958c6e5bb68a75ba0faf2ff7e258","observation_id":"2784a494-0718-47d4-91a8-5373bde43110","resolution":{"observed_at":"2026-05-23T23:53:20.297498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Understanding the difficulty of training deep feedforward neural networks","venue":null,"work_id":"6ea52ecc-810b-4cbf-9a8d-49cb891e0ac7","year":2010},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:c5b7cee1a098633c57e108e7a5e527c99b41a529b032eda1dbc0e8141fc4157a","observation_id":"d0755de6-d1c2-45ca-a195-36108fc475f7","resolution":{"observed_at":"2026-05-23T23:53:20.302169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On the complexity of teaching","venue":null,"work_id":"03c6b517-6228-4e5f-ab54-788853d084a4","year":1995},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:f1a408cf7a8d63bf48d421c8903b38d5b0166494b228a7d4c3cc374847bbb038","observation_id":"2b254278-664d-429e-8700-cb408574ae47","resolution":{"observed_at":"2026-05-23T23:53:20.306425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Smaller coresets for k-median and k-means clustering","venue":null,"work_id":"acd1b9e2-65a8-48ff-b5a5-edbc3dbd183f","year":2007},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:ce0ff3f831f5c2fcfa3b9c8a65883fba7daabb940823f065b02c37a9547e25c4","observation_id":"dbdc1a53-4557-4b0e-a087-1143f15ff861","resolution":{"observed_at":"2026-05-23T23:53:20.310917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification","venue":null,"work_id":"a74f28f7-0a90-4f98-a98f-2227261a3176","year":2015},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:8c6db4569c1d4dcc60a087fb357c35bc8cd3ba17a54adcbc1641fa0615d127e6","observation_id":"c1f2e8cc-2f35-4282-a4a4-8eb7effe44dd","resolution":{"observed_at":"2026-05-23T23:53:20.314927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf","venue":null,"work_id":"aa8dc296-18f6-4849-ba3b-864e4a864318","year":1998},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:4cb793a253990b8291a430c3e949f8af53949acc64a644a5d55ce590511a3fe2","observation_id":"f0df9474-ad4a-4de6-b1a5-78fea62acc17","resolution":{"observed_at":"2026-05-23T23:53:20.319852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Distilling the knowledge in a neural network","venue":null,"work_id":"0d3ce790-0a3c-4de1-8c9a-c160e7fde7b7","year":2015},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:ece9b62351fd56b1a98e19bddbc668e968a7672fcf7285c38e24e46c8abc8368","observation_id":"8215f657-e674-4358-b661-bba088e6b9d9","resolution":{"observed_at":"2026-05-23T23:53:20.324629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","venue":null,"work_id":"2e9dc692-2f8b-4128-8262-1ff2aec93952","year":2017},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:e088507763e385c38d97ae4860f31f71acfcd488e26c87729115d4376623d421","observation_id":"5280ae10-2c88-42ed-bbe9-a3f98c97c117","resolution":{"observed_at":"2026-05-23T23:53:20.328961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"622e9dd3-a94c-41e8-8c6b-e6de7c32a72b","year":1994},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:7fea1b99253bd40a5d154a916233c4d475b9dcd8ebb7a1f4a074a9fc6bf3afad","observation_id":"3c805403-0bec-4889-a422-3a5ec18ce3fa","resolution":{"observed_at":"2026-05-23T23:53:20.333357Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":"0eedb392-16ce-4601-a936-8b9c6cea2f94","year":2015},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:86ecf4d9c2351b7b5ffe0707b808d939dea3be79a4f2de3bf50decb1c7390f08","observation_id":"b6fd83ea-918c-4c96-92de-bfd4b3accaf6","resolution":{"observed_at":"2026-05-23T23:53:20.341646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Understanding black-box predictions via influence functions","venue":null,"work_id":"e5764c48-30c2-4e04-87df-5b9604eb7dc2","year":2017},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:276eb5716ee544a8491ad335f55bbf20fd9526cb4962e5348d204f01cc369073","observation_id":"a35317c2-eb99-49a9-9e7d-d1344d48de73","resolution":{"observed_at":"2026-05-23T23:53:20.346202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"cuda-convnet: High-performance c++/cuda implementation of convolutional neural networks","venue":null,"work_id":"0dee0f16-19ca-4849-9288-78100f416070","year":2012},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:81ee19230ceb625b6aec443148032847871d9865b64f6535b5c89c234d40b78d","observation_id":"4ceb9c7d-c9d0-4a0e-87f6-f322975a555d","resolution":{"observed_at":"2026-05-23T23:53:20.353761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":"be1bd215-dd7a-4bc5-b7f1-21164e14cd32","year":2009},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:be3a4d66ec1d1f8b626a44008e739024b381db00431fd20fc82434fe302ef7b5","observation_id":"77584850-fc23-420c-bfb2-ab95120621d4","resolution":{"observed_at":"2026-05-23T23:53:20.358352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Imagenet classification with deep convolutional neural networks","venue":null,"work_id":"626a067c-2eea-4f01-a63b-bb5f6c80c5fa","year":2012},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":99,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:53d0ed85542a40361460311733759387b77678085e13505afdcc869ec876f6fd","observation_id":"366e5151-7615-474d-9f00-3deeba1c7f10","resolution":{"observed_at":"2026-05-23T23:53:20.362533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1311.6510","last_updated":"2013-11-25T22:59:24Z","snapshot_observed_at":"2026-07-06T03:29:11.747937Z","submitted_at":"2013-11-25T22:59:24Z","title":"Are all training examples equally valuable?","version":1},"cited_work":{"arxiv_id":"1311.6510","doi":null,"metadata_source":"pith","pith_arxiv_id":"1311.6510","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Are all training examples equally valuable?","venue":"cs.CV","work_id":"0e767714-48a5-4303-8c2f-a55cbde5f679","year":2013},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"cited_paper":"/paper/1311.6510","citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:c047b74bf209d91522865b71fb4a5b574834bd6cc146ef9049a69ca6a8cdcbf6","observation_id":"6a2b35a7-b47f-4df2-9f97-dd9494d49884","resolution":{"observed_at":"2026-05-23T23:53:19.826507Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The mnist database of handwritten digits","venue":null,"work_id":"239d6932-118f-45aa-aa48-947f908b747c","year":1998},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":101,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:5ac2d31b3dfd681284fb7d0b23b64b98f11be5f0ee4c2b541e74f9ecb8c9d90c","observation_id":"c1efc577-60e8-480a-91e6-ec265f8a8fae","resolution":{"observed_at":"2026-05-23T23:53:20.366750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Gradient-based learning applied to document recognition","venue":null,"work_id":"90685caa-6231-4472-a942-b44c933cfa1f","year":1998},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":102,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:bf81d0a78cb3e41e120f0d25e9d5a44f1c86c49537816f3952c2db10f54cf934","observation_id":"27f40dcb-4f74-40bd-ab63-eceefd1f136c","resolution":{"observed_at":"2026-05-23T23:53:20.370976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Data poisoning attacks on factorization-based collaborative filtering","venue":null,"work_id":"bc9a879f-9840-458c-aa02-98752a22da5f","year":2016},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":103,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:e1067ad281dc35e22925413877f039245939b88e9717f410cb19ffd85cbd3497","observation_id":"3027fea9-c822-4dcf-84a1-6435d76e90c2","resolution":{"observed_at":"2026-05-23T23:53:20.375017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.07535","last_updated":"2017-11-23T16:28:48Z","snapshot_observed_at":"2026-08-03T17:45:35.865535Z","submitted_at":"2017-10-19T16:04:05Z","title":"Data-Free Knowledge Distillation for Deep Neural Networks","version":2},"cited_work":{"arxiv_id":"1710.07535","doi":null,"metadata_source":"pith","pith_arxiv_id":"1710.07535","snapshot_observed_at":"2026-06-29T18:33:50.595705Z","title":"Data-Free Knowledge Distillation for Deep Neural Networks","venue":"cs.LG","work_id":"1dc26b23-3fc5-49c3-b776-c482894f36be","year":2017},"citing_paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation","version":3},"reference_index":104,"source":"arxiv_source","source_observed_at":"2026-05-23T23:53:19.702198Z"},"links":{"cited_paper":"/paper/1710.07535","citing_paper":"/paper/1811.10959"},"observation_digest":"sha256:6abe0b22acc659babab95c3d119d770372f8148cfc2fb994e0e64aa5bc54dda3","observation_id":"3bbdfe05-ce84-4fce-a053-8cd319940d9b","resolution":{"observed_at":"2026-05-23T23:53:19.791235Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1811.10959","last_updated":"2020-02-24T23:25:50Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T07:17:20.813296Z","submitted_at":"2018-11-27T13:17:45Z","title":"Dataset Distillation"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":6,"parse_uncertain":0,"unresolved":29,"verified_exact":3,"verified_fuzzy":62},"total_outbound_references":124},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 100 of 124 outbound references and 61 inbound Pith citation observations for arXiv:1811.10959."}