{"as_of":"2026-08-15T21:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6e2475c022517fea4f1f6357ee7ee046274cee949b873e740d4b9104a9e348c8","coverage":[{"denominator":69,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":69,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T16:10:09.144752Z","state":"measured"},{"denominator":71,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":71,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T11:04:17.549829Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T02:16:26.916376Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"cited_work":{"arxiv_id":"2508.18903","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.18903","snapshot_observed_at":"2026-07-02T02:16:26.916376Z","title":"Distance-informed neural processes","venue":null,"work_id":"d24d2511-5dd4-4d98-84d4-8033bcbeabc7","year":2025},"citing_paper":{"arxiv_id":"2605.09498","last_updated":"2026-05-10T12:17:29Z","snapshot_observed_at":"2026-08-11T06:57:05.672540Z","submitted_at":"2026-05-10T12:17:29Z","title":"Spectral Transformer Neural Processes","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-12T04:04:14.880310Z"},"links":{"cited_paper":"/paper/2508.18903","citing_paper":"/paper/2605.09498"},"observation_digest":"sha256:88a373dff5b40586fad222584e9b209c441c30df027db17229a40bb9340a9812","observation_id":"81f990df-7933-4480-93f5-a7fc82e60ebc","resolution":{"observed_at":"2026-05-12T06:41:34.789544Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"cited_work":{"arxiv_id":"2508.18903","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.18903","snapshot_observed_at":"2026-07-02T02:16:26.916376Z","title":"Distance-informed neural processes","venue":null,"work_id":"d24d2511-5dd4-4d98-84d4-8033bcbeabc7","year":2025},"citing_paper":{"arxiv_id":"2606.03355","last_updated":"2026-06-02T09:04:43Z","snapshot_observed_at":"2026-08-08T03:55:32.878052Z","submitted_at":"2026-06-02T09:04:43Z","title":"APIC: Amortized Physics-Informed Calibration using Neural Processes","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-06-28T11:04:17.549829Z"},"links":{"cited_paper":"/paper/2508.18903","citing_paper":"/paper/2606.03355"},"observation_digest":"sha256:30df2954936decb714e9dc5874a244bae114f24690bd381baacf2b4bfe73653e","observation_id":"4d06f26e-576b-424a-85c0-075a093bf034","resolution":{"observed_at":"2026-07-02T02:16:26.917939Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2508.18903/citation-record","integrity":"/paper/2508.18903/integrity","json":"/paper/2508.18903/citation-record.json","paper":"/paper/2508.18903"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:10.250098Z","title":"A review of uncertainty quantification in deep learning: Techniques, applications and challenges","venue":null,"work_id":"a465eb7a-5454-4ed0-a53b-4feeb03bb0b0","year":2021},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.844560Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:30e1f0138c9e129a2012dcb904b0f7a9b33457d5583856705872e73ecc3d9e38","observation_id":"40cb5d6b-c55d-4e59-955a-1b81009e6df0","resolution":{"observed_at":"2026-08-05T16:10:10.255320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:10.229119Z","title":"Invertible residual networks","venue":null,"work_id":"8b86d705-e192-455b-8b5b-4950f84254f0","year":2019},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.849550Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:1f2fe16e424b9299fe68b9cebbee495291097e5228a3e4b1fbe02e531accf922","observation_id":"ac4e568f-3203-4f6b-872e-f5e40323a2ea","resolution":{"observed_at":"2026-08-05T16:10:10.234279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:10.211607Z","title":"Weight uncertainty in neural network","venue":null,"work_id":"28aaf43e-a87d-4124-a4a6-0fe798b7b9ad","year":2015},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.854422Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:e6c922c836f5ce528824602139fe72307a1439573383d94ebce43ec857320493","observation_id":"565864ac-e243-462e-9760-0fc1782fbe37","resolution":{"observed_at":"2026-08-05T16:10:10.217639Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.07093","last_updated":"2017-02-06T18:46:50Z","snapshot_observed_at":"2026-08-14T21:38:36.194993Z","submitted_at":"2016-09-22T18:07:56Z","title":"Neural Photo Editing with Introspective Adversarial Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.07093","snapshot_observed_at":"2026-08-05T16:10:08.859003Z","title":"Neural photo editing with introspective adversarial networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.859003Z"},"links":{"cited_paper":"/paper/1609.07093","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:b24185f67a59fa8a6a1b5435b21410fd49408bc2deda81c6f2a9112a8884e299","observation_id":"be91805a-80d4-4d6a-9127-6b000ab59f55","resolution":{"observed_at":"2026-08-05T16:10:08.859003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:10.193259Z","title":"Deep learning in computer vision: A critical review of emerging techniques and application scenarios","venue":null,"work_id":"2c0eec22-8333-49da-982d-f49d5231164f","year":2021},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.864194Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:8682a9eb2187e14dd12d799fb309bdf69cb2a9664c62a388c32c9f26323e16d4","observation_id":"a1d04d24-cea8-4800-8cde-af2f0be6767e","resolution":{"observed_at":"2026-08-05T16:10:10.199439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.07289","last_updated":"2016-02-22T07:02:58Z","snapshot_observed_at":"2026-08-14T22:21:43.095256Z","submitted_at":"2015-11-23T15:58:05Z","title":"Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.07289","snapshot_observed_at":"2026-08-05T16:10:08.868744Z","title":"Fast and accurate deep network learning by exponential linear units (elus)","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.868744Z"},"links":{"cited_paper":"/paper/1511.07289","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:8c9a69ce8c192d47fce4516ea7e0c603fa85923c9b1a650744adc103cdebcb02","observation_id":"fb5c625d-3509-4e3b-a12e-354cae9e5fae","resolution":{"observed_at":"2026-08-05T16:10:08.868744Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:10.172035Z","title":"Predicting chemical parameters of river water quality from bioindicator data","venue":null,"work_id":"616a1415-0f54-4cbe-8d6e-a33f784bdade","year":2000},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.874037Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:82db8650db69cb10bbcb6c317289f88645cca67b4bfe20df18be69dbaa0c37d7","observation_id":"ac7cf1f3-96b9-4e04-9947-a01279da6bc3","resolution":{"observed_at":"2026-08-05T16:10:10.178042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:10.154567Z","title":"Meta-learning stationary stochastic process prediction with convolutional neural processes","venue":null,"work_id":"db3eaffa-54d8-4319-983e-bc96dc677ce9","year":2020},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.878078Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:ea313fbc2b78b3bf03fa30fb9e51b2799125ee1e8882e9c7170b8b0705d2d11e","observation_id":"70924ecd-09ae-410b-9d6e-0e7a31a3ff0c","resolution":{"observed_at":"2026-08-05T16:10:10.159091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:10.137973Z","title":"An introduction to deep reinforcement learning","venue":null,"work_id":"6d4ddb7a-88fb-4d7c-b0b7-a86620767c70","year":2018},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.882172Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:a8caaf741abf4350591ea276792a3e5d7fcf6e22e7675c66732462d610fa574e","observation_id":"02bf9396-1815-4fdf-bcea-76eb2e0e00ad","resolution":{"observed_at":"2026-08-05T16:10:10.143593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:10.122268Z","title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","venue":null,"work_id":"790d95e1-84f4-42dc-a873-90c91ca251e3","year":2016},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.886067Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:b98b890928c41c4491eca0e2fcf2dbfdb23d7832f82df304992ae32f46ac356e","observation_id":"7343f5c8-6e0b-41db-b251-7daabcc1feee","resolution":{"observed_at":"2026-08-05T16:10:10.127175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:10.106022Z","title":"Conditional neural processes","venue":null,"work_id":"98c27c63-d73c-46b6-9e92-58a58ac15f1a","year":2018},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.890217Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:22f0c01c9c3022a66c30ad4232871e28b25fbcd524de12d0f4237d5570066c38","observation_id":"b31b3f80-639e-44e3-8628-67f7a67e5615","resolution":{"observed_at":"2026-08-05T16:10:10.111506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.01622","last_updated":"2018-07-04T14:49:46Z","snapshot_observed_at":"2026-08-14T18:55:44.864275Z","submitted_at":"2018-07-04T14:49:46Z","title":"Neural Processes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.01622","snapshot_observed_at":"2026-08-05T16:10:08.894233Z","title":"Neural processes","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.894233Z"},"links":{"cited_paper":"/paper/1807.01622","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:9faf132eacdf9d05e724dddfbb16459ab044f1afe88f2234c10f13d2460cc9a5","observation_id":"635ab363-40b8-41da-a493-d59eda472981","resolution":{"observed_at":"2026-08-05T16:10:08.894233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:10.088316Z","title":"Function contrastive learning of transferable meta-representations","venue":null,"work_id":"347fec07-e997-4986-b68d-f0c1ab45a1e4","year":2021},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.898845Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:05ecb81ae147ee7c47033c066a376310dc94a555f20a7884ff4b8257de1f4c55","observation_id":"af87aac1-5aef-4aac-aedc-e495d4fb5640","resolution":{"observed_at":"2026-08-05T16:10:10.094582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13556","last_updated":"2020-06-25T13:20:06Z","snapshot_observed_at":"2026-08-14T08:04:21.632351Z","submitted_at":"2019-10-29T21:56:00Z","title":"Convolutional Conditional Neural Processes","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.13556","snapshot_observed_at":"2026-08-05T16:10:08.903339Z","title":"Convolutional conditional neural processes.arXiv preprint arXiv:1910.13556, 2019","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.903339Z"},"links":{"cited_paper":"/paper/1910.13556","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:cbb43f7f087b6afef74062a51e4d4386a09428308d226e976825d3b6455e9337","observation_id":"1b2f9377-a100-4b5c-89aa-949532f94328","resolution":{"observed_at":"2026-08-05T16:10:08.903339Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:08.907699Z","title":"Practical variational inference for neural networks.Advances in neural information processing systems, 24, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.907699Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:c70ba9e6480c84adc23964c33ae6406cca7d9198e4e87066268888cecbc0fbbb","observation_id":"e027dead-7a52-4935-b444-eb23fb8b39db","resolution":{"observed_at":"2026-08-05T16:10:08.907699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:10.061086Z","title":"Improved training of wasserstein gans","venue":null,"work_id":"3ebf2b12-24d9-46a0-a023-cda3d3f4099a","year":2017},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.911717Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:a39f3e0039eb81e26a9610ce6f31e38fd3837f541ad0e870f6de80bb5bcd6595","observation_id":"653d2c1b-c4e5-4028-8027-81884d8bb0d0","resolution":{"observed_at":"2026-08-05T16:10:10.066752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:08.915612Z","title":"On calibration of modern neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.915612Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:3005389ce654c3b0a7f840003467f31294cf0c5a9c7289d17c1666433b799c0b","observation_id":"dacefcd9-511e-49bc-99cc-6ddb23b28606","resolution":{"observed_at":"2026-08-05T16:10:08.915612Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:10.035071Z","title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification","venue":null,"work_id":"3049ae48-35a8-48a0-b0a9-32dc7b80809a","year":2015},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.921007Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:8af8c0621caeb26dcca87d9f73f3185e7ca67d5685f6e745356c6315a2c0f961","observation_id":"8e54306f-dee4-40ee-b5d3-72983d973819","resolution":{"observed_at":"2026-08-05T16:10:10.040091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.02729","last_updated":"2019-08-07T17:04:26Z","snapshot_observed_at":"2026-08-15T00:47:18.395562Z","submitted_at":"2019-08-07T17:04:26Z","title":"Robust Learning with Jacobian Regularization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.02729","snapshot_observed_at":"2026-08-05T16:10:08.925167Z","title":"Robust learning with jacobian regularization","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.925167Z"},"links":{"cited_paper":"/paper/1908.02729","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:e465f87ff13df78c1401a496d2983815355be60d8dfb4cc54952c27dafaa8cd9","observation_id":"84743644-c89c-4326-a05f-c1325da33a79","resolution":{"observed_at":"2026-08-05T16:10:08.925167Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:10.017399Z","title":"Meta-learning in neural networks: A survey","venue":null,"work_id":"1511d7cc-6e93-419c-8b48-2d97b771ec8a","year":2021},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.929591Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:550e4561151ee1b6e316a92b70cfaf5d08fb8e5438fcd0e2acc66fa7eebb68fd","observation_id":"2b9c2b36-5e52-4067-802c-9b0aa556f44d","resolution":{"observed_at":"2026-08-05T16:10:10.023414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07088","last_updated":"2018-02-20T12:38:49Z","snapshot_observed_at":"2026-08-14T19:44:10.906616Z","submitted_at":"2018-02-20T12:38:49Z","title":"i-RevNet: Deep Invertible Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07088","snapshot_observed_at":"2026-08-05T16:10:08.933409Z","title":"i-revnet: Deep invertible networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.933409Z"},"links":{"cited_paper":"/paper/1802.07088","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:057a0af758939a7b36f5a415985a17f2484cff6dec9899f2139f4c6ecceb0832","observation_id":"70dcb92f-147f-4a63-b2ac-db8bf2db5cac","resolution":{"observed_at":"2026-08-05T16:10:08.933409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:08.937720Z","title":"Transformers are rnns: Fast autoregressive transformers with linear attention","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.937720Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:5fb5a9950a203a58aa23a91e9ea322528901289a624f3af401d618236d955716","observation_id":"845cbea4-2137-47ab-970d-295bc84b3ec5","resolution":{"observed_at":"2026-08-05T16:10:08.937720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:08.941611Z","title":"What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.941611Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:7ee82768f392f99c573e33f2e95bab77dc71ca1e5a303851d203eab88a1786d3","observation_id":"b1f48777-8bdf-4521-bbbb-cb837b361534","resolution":{"observed_at":"2026-08-05T16:10:08.941611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:08.945488Z","title":"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.945488Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:7bee04fd6949e927bf1c54d3647d8f8b771ec927346b85f2b46cb19f736ac6ca","observation_id":"911a0ab4-7e71-4ed1-a317-d7ac9cfc888a","resolution":{"observed_at":"2026-08-05T16:10:08.945488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1901.05761","last_updated":"2019-07-09T10:49:01Z","snapshot_observed_at":"2026-08-14T17:29:40.746498Z","submitted_at":"2019-01-17T12:37:26Z","title":"Attentive Neural Processes","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.05761","snapshot_observed_at":"2026-08-05T16:10:08.949605Z","title":"Attentive neural processes.arXiv preprint arXiv:1901.05761, 2019","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.949605Z"},"links":{"cited_paper":"/paper/1901.05761","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:2e6b644e39fd35776559a0abcc4864d0e3fea87a592b76c6df7bd2d9d10d8410","observation_id":"d3c351a4-368c-4e03-9e93-4ef0dc720a92","resolution":{"observed_at":"2026-08-05T16:10:08.949605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-05T16:10:08.954318Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.954318Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:e0668b7455863fa0291144f448d201f6700901463575302849c8efabfb014b40","observation_id":"0ad9f453-c2a1-4d94-9af9-e8b17e535a35","resolution":{"observed_at":"2026-08-05T16:10:08.954318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:09.969776Z","title":"An introduction to variational autoencoders","venue":null,"work_id":"e42bf16d-ce26-40d5-a608-05b57130656b","year":2019},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.958501Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:61f4957906c2338c20484c142cc047a5a2d68c50dd2ff6d800eb4851d6d48142","observation_id":"94bfb745-3a79-43f8-bad7-4cf70b670690","resolution":{"observed_at":"2026-08-05T16:10:09.974336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.952372Z","title":"Toward the optimal preconditioned eigensolver: Locally optimal block preconditioned conjugate gradient method","venue":null,"work_id":"ceb28eca-8c19-4f93-87bb-60370ac08e08","year":2001},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.963228Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:bc2d718ecdccfe2ff370b60cbd308c9b6b396346313b2607c248335b36da6d28","observation_id":"7b7c299e-d9a2-4fd4-adbc-db8f06527c8c","resolution":{"observed_at":"2026-08-05T16:10:09.957977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:08.969278Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.969278Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:96adc47592f78a751a8ce3f1e03732669d9a14dabe2c6241cf08d2e14a36a63d","observation_id":"762a90a5-384b-467e-a639-d2af5d1e7777","resolution":{"observed_at":"2026-08-05T16:10:08.969278Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:08.974571Z","title":"Accurate uncertainties for deep learning using calibrated regression","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.974571Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:bc2c507b4e48ebdadb4fdca40f6e610e63eed709b156f9763375d71dda292535","observation_id":"4c5af11f-3f79-4276-bc92-dd9265f6ea7b","resolution":{"observed_at":"2026-08-05T16:10:08.974571Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:08.979222Z","title":"Simple and scalable predictive uncertainty estimation using deep ensembles","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.979222Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:932d001c7972c1c05be2f56e86cdc6ffc5623ab0095d74e493567d2f006a06ca","observation_id":"3577d5cf-8007-4c20-9517-756e68bd259c","resolution":{"observed_at":"2026-08-05T16:10:08.979222Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:08.983646Z","title":"Tiny imagenet visual recognition challenge","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.983646Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:291d03e9521c1e10dd2177455d136142ac2c15964b88eca752d0c3de8de9c015","observation_id":"116711ad-2c51-4a93-8fed-6736cc204e7e","resolution":{"observed_at":"2026-08-05T16:10:08.983646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:09.897943Z","title":"Bootstrapping neural processes","venue":null,"work_id":"d5565049-a2d8-4df9-b5fc-5255d28bb53f","year":2020},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.987919Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:41ad0ba74796e138375c7d1e871b35f98048ba6b64f064437b552cef0ef54dd3","observation_id":"6c6afc1b-3698-4038-83f4-c59c1c33ebfb","resolution":{"observed_at":"2026-08-05T16:10:09.902250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.883828Z","title":"The ecological role of volterra’s equations","venue":null,"work_id":"e1ea0037-e818-4f4f-86cb-9b46ca325daf","year":1968},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.994467Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:c7d0ca96ce43da647d2bcb2edde2eeddc3e006b0b3f418ebe8fd60f12e26d503","observation_id":"f7b8678b-46fd-4acb-803e-756c8d7a1b77","resolution":{"observed_at":"2026-08-05T16:10:09.888097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.869887Z","title":"Simple and principled uncertainty estimation with deterministic deep learning via distance awareness","venue":null,"work_id":"37889153-910e-46fd-bb5b-826939aedb9a","year":2020},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:08.999570Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:c934b12159e74738e7620123a959276decf762aed096ad05632d06afabb7d9e2","observation_id":"314cbf5f-7be5-4a03-ba7f-cc65aac3b6d1","resolution":{"observed_at":"2026-08-05T16:10:09.874294Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.855428Z","title":"The functional neural process","venue":null,"work_id":"886d0c2e-fe33-4edc-8905-b6d3119f970d","year":2019},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.003968Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:a3d93db583651df75e01269b934a6cc521ee39607bc44530e39908372333ded1","observation_id":"dd6e0847-9d00-4eb2-a35f-c54bad1e823f","resolution":{"observed_at":"2026-08-05T16:10:09.860085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.840229Z","title":"Rectifier nonlinearities improve neural network acoustic models","venue":null,"work_id":"bbe0cbbb-065a-4114-a83b-bf60cea327da","year":2013},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.008300Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:28e829fba0dcb8f5c7cc2d9e005402b141b8ae81f334bb3171d2711d56820466","observation_id":"7cbc0338-e0b5-4653-80fc-0314fc817228","resolution":{"observed_at":"2026-08-05T16:10:09.844774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.05957","last_updated":"2018-02-16T14:41:39Z","snapshot_observed_at":"2026-08-15T15:47:46.409113Z","submitted_at":"2018-02-16T14:41:39Z","title":"Spectral Normalization for Generative Adversarial Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.05957","snapshot_observed_at":"2026-08-05T16:10:09.012642Z","title":"Spectral normalization for generative adversarial networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.012642Z"},"links":{"cited_paper":"/paper/1802.05957","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:68354ad8399362378d05c396ecf8e244494838e89dd6d679b802d43f9ea922f6","observation_id":"b631c2a2-6783-4263-b87c-ccbe67c7bf00","resolution":{"observed_at":"2026-08-05T16:10:09.012642Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.04179","last_updated":"2023-02-07T23:50:34Z","snapshot_observed_at":"2026-08-13T15:07:54.021923Z","submitted_at":"2022-07-09T02:28:58Z","title":"Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.04179","snapshot_observed_at":"2026-08-05T16:10:09.017509Z","title":"Transformer neural processes: Uncertainty-aware meta learning via sequence modeling","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.017509Z"},"links":{"cited_paper":"/paper/2207.04179","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:aeb6d68f72fd3db4f37d92ba8a57cd171bb8b404af2d6918ca269e9a55bdfa5e","observation_id":"6deb9306-d9c6-4386-8bd3-cdb83850f3ce","resolution":{"observed_at":"2026-08-05T16:10:09.017509Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:09.825108Z","title":"Stochastic differential equations","venue":null,"work_id":"93b22a97-32c0-4305-b3b2-eae18cde358c","year":2003},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.022438Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:7d3756fbf55cff2c78040e6e67a1d78e0130b08888243ea72aa76218a5bb90ba","observation_id":"d96aabf4-abeb-4045-9e4f-2665f3e84286","resolution":{"observed_at":"2026-08-05T16:10:09.829535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.798528Z","title":"A survey of the usages of deep learning for natural language processing","venue":null,"work_id":"c00daa23-78e6-40b3-9c9e-9afa496a9837","year":2020},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.027149Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:4f483077fbea4fda7bd9a924ae24d4229b57bdb7ec22d437f5bf8256ff07cf5b","observation_id":"4653b16c-0be1-4ee7-9b99-22ba27434578","resolution":{"observed_at":"2026-08-05T16:10:09.812229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.01703","last_updated":"2019-12-03T22:06:05Z","snapshot_observed_at":"2026-07-06T08:41:49.632205Z","submitted_at":"2019-12-03T22:06:05Z","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01703","snapshot_observed_at":"2026-08-05T16:10:09.031384Z","title":"Pytorch: An imperative style, high-performance deep learning library","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.031384Z"},"links":{"cited_paper":"/paper/1912.01703","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:3ba751eb67f06203b62b7cece1ead72c935af0e95539fc9d50913fe72ed80cec","observation_id":"da589ca2-8dce-48b3-b4ef-e7c013c07102","resolution":{"observed_at":"2026-08-05T16:10:09.031384Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:09.782839Z","title":"Fubini’s theorem and tonelli’s theorem","venue":null,"work_id":"ee08b7f1-26df-465c-b8ba-67129b248f02","year":1997},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.036165Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:44e79bd6b345a81b70d6dc72262a934865160916b9a0305f80c01a17c6b760c3","observation_id":"860e096b-8860-4436-81c2-ad9c94470687","resolution":{"observed_at":"2026-08-05T16:10:09.787750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.767372Z","title":"Convolutional neural networks applied to house numbers digit classification","venue":null,"work_id":"b5184136-aa78-4a57-852b-2b5a49113f2f","year":2012},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.040264Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:9e5e279c30484de67966bc59f761e51fe5c666b9fcec4d9f2cba0b490d5ccdef","observation_id":"1ebaf7b4-83c5-4841-bd76-2e0c1b087f95","resolution":{"observed_at":"2026-08-05T16:10:09.772246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.02731","last_updated":"2019-01-08T13:03:14Z","snapshot_observed_at":"2026-08-14T17:33:46.262116Z","submitted_at":"2019-01-08T13:03:14Z","title":"A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.02731","snapshot_observed_at":"2026-08-05T16:10:09.044134Z","title":"A comprehensive guide to bayesian convolutional neural network with variational inference","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.044134Z"},"links":{"cited_paper":"/paper/1901.02731","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:578c96e7b17b421c8caceedc38c017a51ed39065b597e65a787effcbd2228abd","observation_id":"59d2c88b-8f13-4d04-9b41-5c5896a3281c","resolution":{"observed_at":"2026-08-05T16:10:09.044134Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-14T23:20:42.336514Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-05T16:10:09.048239Z","title":"Very deep convolutional networks for large-scale image recognition","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.048239Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:c5df4f71617bf03a7f6ca67b7244fca1ea8ac754cbcac5b43032b3bf50f41d0b","observation_id":"56f57b6a-7dd1-4f62-a3ea-051ad6c5e0ee","resolution":{"observed_at":"2026-08-05T16:10:09.048239Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:09.052158Z","title":"Sparse gaussian processes using pseudo-inputs","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.052158Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:a3a32f4e183c2b988a4ed1e2e2a1dd2c87e0520aec173c27fd028afad75efae9","observation_id":"22d9e402-3c7f-48e7-a312-0c765deb4c13","resolution":{"observed_at":"2026-08-05T16:10:09.052158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:09.742421Z","title":"Multi-target regression via input space expansion: treating targets as inputs","venue":null,"work_id":"d4233b89-9b65-41be-9420-c6d9930f0012","year":2016},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.055927Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:7cc63bb040a80b2d871426c0f1492f0099ae3e3864bb5a2bb197cb5e5ef7477f","observation_id":"aff547d3-6742-4d6a-866f-06633128a925","resolution":{"observed_at":"2026-08-05T16:10:09.746928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.727346Z","title":"A block orthogonalization procedure with constant synchronization requirements","venue":null,"work_id":"a807de8b-9a86-455c-87b8-efe9b97617b2","year":2002},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.059700Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:2b355be9753a940f3259bd97f09a84f5585f3f76d2576f5980d9ac4863ccdde4","observation_id":"b4c2011e-39ff-4258-a66d-bf45653c3947","resolution":{"observed_at":"2026-08-05T16:10:09.732231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.712515Z","title":"Exploiting inferential structure in neural processes","venue":null,"work_id":"178baf55-5ae8-4319-92ff-e4d9fd5ab657","year":2089},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.063868Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:00de8e822505c9ff38e2d6c7084bc0a5b8b02b677ab95cccc3cb4c22bd52472b","observation_id":"4dd1c6ef-188d-49a7-991b-74aaa928bc86","resolution":{"observed_at":"2026-08-05T16:10:09.716793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.696092Z","title":"Sparse sinkhorn attention","venue":null,"work_id":"f41f1b47-2d71-4a0c-a2af-90cc4939986b","year":2020},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.068494Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:57af4150a62e14fc6bca6b631e012254493192ac533ca7af0b924e8a8699fea6","observation_id":"ab187c5e-8814-40de-9b90-4acccccc3275","resolution":{"observed_at":"2026-08-05T16:10:09.702148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:09.072373Z","title":"Variational learning of inducing variables in sparse gaussian processes","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.072373Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:deba391ca57f3436b7e800a106312e4834aad6b9271be31be107047cc5e37fc7","observation_id":"dc1d1447-0866-42a5-be02-16861acc0dd0","resolution":{"observed_at":"2026-08-05T16:10:09.072373Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.11409","last_updated":"2022-03-07T12:54:27Z","snapshot_observed_at":"2026-08-10T01:16:00.200780Z","submitted_at":"2021-02-22T23:29:12Z","title":"On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty","version":3},"cited_work":{"arxiv_id":"2102.11409","doi":null,"metadata_source":"pith","pith_arxiv_id":"2102.11409","snapshot_observed_at":"2026-08-05T16:10:09.242061Z","title":"On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty","venue":"cs.LG","work_id":"804895b5-c05a-4630-b4e8-14d494d08667","year":2021},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.076294Z"},"links":{"cited_paper":"/paper/2102.11409","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:7953b7a3a87f7d371ced9eca7b2104a18d3d99a134ea78456bf034f017da3448","observation_id":"5a7d330e-f99f-40d9-aa9d-8b1d8a6c3a12","resolution":{"observed_at":"2026-08-05T16:10:09.247171Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.669368Z","title":"Uncertainty estimation using a single deep deterministic neural network","venue":null,"work_id":"5b75c5be-1194-42c7-b871-450e71a673c8","year":2020},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.080274Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:003b70a0733e628d652df098934c2e6b4ef47bd191720f178b8a63f5e831023e","observation_id":"7b5d7203-42cd-4f0f-8175-27cfafa74896","resolution":{"observed_at":"2026-08-05T16:10:09.675028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:09.084006Z","title":"Attention is all you need","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.084006Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:2c260904f31507eea826a52b2a72ea8052b2d773fb2523ee1b8345365ef20e3d","observation_id":"a8f59007-f291-4f02-bb05-bcaea7027033","resolution":{"observed_at":"2026-08-05T16:10:09.084006Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:09.641883Z","title":"Gaussian latent representations for uncertainty estimation using mahalanobis distance in deep classifiers","venue":null,"work_id":"76649ce1-fe24-4bdb-87c6-73c999fd9eb8","year":2023},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.087591Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:ca4af0720df00d664ce3eaee3bddd72afab34f3ec5aea189f479c266e8e1d78c","observation_id":"68885b94-95ee-4183-907f-e8486c75c44e","resolution":{"observed_at":"2026-08-05T16:10:09.648212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.05163","last_updated":"2025-07-04T18:13:55Z","snapshot_observed_at":"2026-08-14T16:54:43.912983Z","submitted_at":"2025-05-08T11:57:35Z","title":"Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.05163","snapshot_observed_at":"2026-08-05T16:10:09.092184Z","title":"Probabilistic embeddings for frozen vision-language models: Uncertainty quantification with gaussian process latent variable models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.092184Z"},"links":{"cited_paper":"/paper/2505.05163","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:62039074f87fe6a4ea751a1fca4c988871a5732dbf9e520e4524af344881d3af","observation_id":"dec8983c-6a67-4c2f-a8b0-3749a9aec775","resolution":{"observed_at":"2026-08-05T16:10:09.092184Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:09.624552Z","title":"Integrating visual and semantic similarity using hierarchies for image retrieval","venue":null,"work_id":"d639c87e-0629-4d71-a21e-3d50a8eea6de","year":2023},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.096282Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:3d0d176a765b20403134219373ef18dd4a002a7f669e2c5afed07f967b6a4fac","observation_id":"eaa8804b-103b-42d0-97e4-8b0cf2394ddc","resolution":{"observed_at":"2026-08-05T16:10:09.629496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.609237Z","title":"Locally weighted projection regression: An o (n) algorithm for incremental real time learning in high dimensional space","venue":null,"work_id":"3618a150-963f-4253-a69d-45871f013f66","year":2000},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.101975Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:3771185b581f98aa1501e2c4aefc35df0844a3e8ebc195e4cb4e3f394127339d","observation_id":"be4d3bdb-e3c7-4098-ad9c-adedbdde24f9","resolution":{"observed_at":"2026-08-05T16:10:09.613528Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.03264","last_updated":"2025-01-04T03:28:21Z","snapshot_observed_at":"2026-08-12T18:58:18.495584Z","submitted_at":"2025-01-04T03:28:21Z","title":"Bridge the Inference Gaps of Neural Processes via Expectation Maximization","version":1},"cited_work":{"arxiv_id":"2501.03264","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.03264","snapshot_observed_at":"2026-08-05T16:10:09.201963Z","title":"Bridge the Inference Gaps of Neural Processes via Expectation Maximization","venue":"cs.LG","work_id":"a4437eb0-e27e-47f5-b8cd-0463f278c2de","year":2025},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.106003Z"},"links":{"cited_paper":"/paper/2501.03264","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:ecdedefcdb2a2b8240ff5cfcc4ed25b3c56d912cbbaedc8fe741519c76b910c5","observation_id":"0021a57a-9aed-4be0-9904-2fa6b3b4dc72","resolution":{"observed_at":"2026-08-05T16:10:09.208359Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.592853Z","title":"Doubly stochastic variational inference for neural processes with hierarchical latent variables","venue":null,"work_id":"9e690573-1633-4964-9e55-efba623bc784","year":2020},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.110186Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:28c5ac1076062a6d3bfa057e82d5fbd8eadd86ca9e99f791d93555d5dc9a0a54","observation_id":"3c08b131-bbfa-410b-ae70-27197972d78a","resolution":{"observed_at":"2026-08-05T16:10:09.597777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.576220Z","title":"Learning expressive meta-representations with mixture of expert neural processes","venue":null,"work_id":"0ac706cc-98f6-4da4-bc57-c6247bc7a9d4","year":2022},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.113927Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:26a2285e4eab245f926aa302874c54ab78e2b7e3f77a8bbe42c774749f931637","observation_id":"577ab1c1-c1d0-4828-aa0f-f9adaf865e6d","resolution":{"observed_at":"2026-08-05T16:10:09.582363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.556451Z","title":"Stochastic modelling for systems biology","venue":null,"work_id":"aa3f2606-735f-4c9e-b8c3-71060d703c8e","year":2018},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.117761Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:a4661fbd1e1e8bfafa3fadc3b0448362d114f33727bf961ddbbf7a9924101a12","observation_id":"a8459448-f78a-4cd4-a548-b359d460423f","resolution":{"observed_at":"2026-08-05T16:10:09.561667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:09.121776Z","title":"MIT press Cambridge, MA, 2006","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.121776Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:d09d05344e442efbe87fcfa4fd686a5a8baf59ad1f3fc76f73efdd143f2d175b","observation_id":"9f16756b-dff5-4ba8-98f7-cf5068a9d164","resolution":{"observed_at":"2026-08-05T16:10:09.121776Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:09.530755Z","title":"Deep kernel learning","venue":null,"work_id":"97253e3a-4f41-4b51-ba11-ebce6c75c3b0","year":2016},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.126331Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:f9e5f17c19f8e653856791dc17bc6f3badf5730109849beee719a268e948d734","observation_id":"5a840652-b349-42ac-be93-77d8196c2154","resolution":{"observed_at":"2026-08-05T16:10:09.535166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.10941","last_updated":"2017-05-31T04:56:25Z","snapshot_observed_at":"2026-08-14T20:57:05.362332Z","submitted_at":"2017-05-31T04:56:25Z","title":"Spectral Norm Regularization for Improving the Generalizability of Deep Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.10941","snapshot_observed_at":"2026-08-05T16:10:09.130484Z","title":"Spectral norm regularization for improving the generaliz- ability of deep learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.130484Z"},"links":{"cited_paper":"/paper/1705.10941","citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:7791a9495fe1ee1db3bef54604a4afd7b5efcf257799ab9f7cbf74a4d49b570a","observation_id":"e31a82e2-7663-437a-a02b-e187e1bbf063","resolution":{"observed_at":"2026-08-05T16:10:09.130484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:10:09.509021Z","title":"Rank-n-contrast: Learning continuous representations for regression","venue":null,"work_id":"e0f0961e-95e0-4a29-9636-7d95f3db9ea9","year":2023},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.135132Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:f232f0289c1b219cd383a2b19695dcc91203cf90878090fcb60f9b0c00e6f96e","observation_id":"617a0c59-cb5f-4335-ae26-27372ba1aa75","resolution":{"observed_at":"2026-08-05T16:10:09.514650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.492625Z","title":", N}; that is, px1:N (y1:N ) = pxπ(1:N ) (yπ(1:N )) (14)","venue":null,"work_id":"5e3c4090-92d6-451f-9897-5160e2ac690a","year":null},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.140711Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:25d1bc853fa2c90db6279409182591167b90e39e673cb971238ca03ea09fda48","observation_id":"84d0bfcf-0509-43d5-9677-39365947b0b3","resolution":{"observed_at":"2026-08-05T16:10:09.497135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T16:10:09.476285Z","title":", yM } of the outputs must be recoverable by integrating out the remaining variables from the joint distribution px1:N (y1:N )","venue":null,"work_id":"8aba2975-6e2a-4a0d-83dd-6130f46ac311","year":null},"citing_paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-05T16:10:09.144752Z"},"links":{"citing_paper":"/paper/2508.18903"},"observation_digest":"sha256:68b2d2300539639feac49c769fd4d0cc796d12029015e1816fa951f1a877e676","observation_id":"1446361f-f210-45e6-acf6-f06927cc8e6b","resolution":{"observed_at":"2026-08-05T16:10:09.481205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.18903","last_updated":"2025-08-26T10:19:36Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T18:42:48.455020Z","submitted_at":"2025-08-26T10:19:36Z","title":"Distance-informed Neural Processes"},"reference_resolution":{"displayed":69,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":28,"verified_exact":2,"verified_fuzzy":39},"total_outbound_references":69},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 2 inbound Pith citation observations for arXiv:2508.18903."}