{"as_of":"2026-08-04T12:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cb30696b46c42f6fdc9e44f7e05dd8bfadbd08e1624972d6fe66cb48909ebd53","coverage":[{"denominator":76,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":76,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-25T12:36:34.595988Z","state":"measured"},{"denominator":76,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":76,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1907.00274/citation-record","integrity":"/paper/1907.00274/integrity","json":"/paper/1907.00274/citation-record.json","paper":"/paper/1907.00274"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Expert gate: Lifelong learning with a network of experts","venue":null,"work_id":"019858f2-82fb-43c5-bf60-13368ebee3ef","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:58b13e56f71ab4c9b381209ccb757fea2739c1b18e5ae067d952fc913a149c16","observation_id":"48e05cde-b385-4bf4-8b21-9cab325fa880","resolution":{"observed_at":"2026-05-25T12:36:58.126621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Do deep nets really need to be deep? In Advances in Neural Information Processing Systems (NeurIPS)","venue":null,"work_id":"b8bcc87a-284a-41ef-9686-023002c37bfe","year":2014},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:11d15c379fa7d216dbc5e3aa6eb61562334c7b75865153122f7cca36aa4ec8eb","observation_id":"65ed71f0-64e4-400e-9a3b-19f3924810ab","resolution":{"observed_at":"2026-05-25T12:36:58.117472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Curriculum learning","venue":null,"work_id":"dc36c5c6-073a-4a5a-8a0a-b4f00803dda2","year":2009},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:37238f262879321a65d1e68506617f646812dfff0fc58ea15f0126316359886b","observation_id":"b7a93167-7432-48ae-aac2-8978b91db702","resolution":{"observed_at":"2026-05-25T12:36:58.113884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Model compression","venue":null,"work_id":"c666b574-d060-4091-99de-cf3fe5c335f3","year":2006},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:af541789790915dc56ba2633edbce30b9a691b5a0056adfe985d2a9c15382685","observation_id":"7a54ce0f-7f1f-4862-a0e5-a4fd681d3ade","resolution":{"observed_at":"2026-05-25T12:36:58.107922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning complexity-aware cascades for deep pedestrian detection","venue":null,"work_id":"d629e3c9-c400-4f19-9d90-066ea1ee5cd9","year":2015},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:dff65bb862ae16a39013b72dc9d7832aa61908b64266f2d11eb5682f282509be","observation_id":"0be40f78-8b70-408b-8fd8-b62ef56574f4","resolution":{"observed_at":"2026-05-25T12:36:58.110664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multitask learning","venue":null,"work_id":"8a14237b-24b0-494e-9aec-35b4f45b3b3e","year":1997},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:f5a0508d4da0bec63da401394e7475ee8c9de2fe78dd7292c4840a6c38e5edc0","observation_id":"b363ac51-0966-4c7a-8f11-ec1e6076b755","resolution":{"observed_at":"2026-05-25T12:36:58.122426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05641","last_updated":"2016-04-23T23:14:39Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:09:20Z","title":"Net2Net: Accelerating Learning via Knowledge Transfer","version":4},"cited_work":{"arxiv_id":"1511.05641","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05641","snapshot_observed_at":"2026-07-09T04:35:57.543400Z","title":"Net2Net: Accelerating Learning via Knowledge Transfer","venue":"cs.LG","work_id":"2834821a-1c72-4b92-b3a6-2570e45901f8","year":2015},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1511.05641","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:45794e86d6414f5f639957bedef52b0cec9215f1a20278aba7480ac057808d36","observation_id":"90988fc7-2396-4064-b4f1-a4388c4c10cf","resolution":{"observed_at":"2026-05-25T12:36:57.690706Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Describing textures in the wild","venue":null,"work_id":"4f507038-64a8-4911-8d39-4dc4673d78e3","year":2014},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:214b6d646b18bd2641ee70ea5fba82bce9da41f14b8ac20161d9d0a991f31775","observation_id":"af882ce8-7fa6-4081-92a9-5402c57f2d76","resolution":{"observed_at":"2026-05-25T12:36:57.946936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":"30a8808b-a8a6-4c75-8524-2a3e686080be","year":2009},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:691f543a5797ebb9ac150f46ea7c1df94ccd43e637149220d32ee57e5ab74402","observation_id":"9af2e924-61e9-490e-aaf6-fbf3831e53e5","resolution":{"observed_at":"2026-05-25T12:36:58.022568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"How do humans sketch objects? ACM Trans","venue":null,"work_id":"2a29000a-75d3-4553-8e9f-369120c3b515","year":2012},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:dfe5c81ad9886c7deadc76498e8ae5b1eb033b6f796718ff3d7f53a4f6a3260a","observation_id":"f554ef34-8853-4dad-9bc3-381740ff63e5","resolution":{"observed_at":"2026-05-25T12:36:57.981143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Everingham, L","venue":null,"work_id":"0b00ed6a-ed3f-4313-bd07-2f3445bb13f6","year":2012},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:d0dab5f7bfae3a443a6b1f0a24f956b7b35560039b438b4d028538587e9f9dfe","observation_id":"26addd81-f77c-4860-b928-3645fef5d283","resolution":{"observed_at":"2026-05-25T12:36:57.999578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.03643","last_updated":"2018-05-09T04:41:26Z","snapshot_observed_at":"2026-07-06T06:38:14.782357Z","submitted_at":"2018-05-09T04:41:26Z","title":"Learning to Teach","version":1},"cited_work":{"arxiv_id":"1805.03643","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.03643","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning to Teach","venue":"cs.LG","work_id":"1bd45ace-e5d7-4ebd-9275-32f8a4811834","year":2018},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1805.03643","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:bde7e9c3f0a6cd7e427736f5be4191cd1a7568671cbeba1522a73ae91e8c40da","observation_id":"1968912d-98e7-4b50-87d9-568f172e8c57","resolution":{"observed_at":"2026-05-25T12:36:57.657208Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Spatially adaptive computation time for residual networks","venue":null,"work_id":"a6776c67-1712-4485-a24a-e87762e6e1cb","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:7710f90f17f4cbfdb6fe4227cc607d82bca542154d09724da7d3a960e90097a9","observation_id":"a7cf161d-a41c-4e0d-ba50-7dc8bd5c3eee","resolution":{"observed_at":"2026-05-25T12:36:57.956428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Unsupervised domain adaptation by backpropagation","venue":null,"work_id":"9493c533-e05f-4635-9ab5-09550e6575b3","year":2015},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:3874ccf5a35960aafbae5fdbe455230bfb92564f14a0793e49cf189c7f009bbb","observation_id":"b37db891-95ae-42c8-9acd-9be762a5e1cf","resolution":{"observed_at":"2026-05-25T12:36:58.104793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Rich feature hierarchies for accurate object detection and semantic segmentation","venue":null,"work_id":"fbaf0120-e860-42e7-942a-9dbbd328970d","year":2014},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:428a441ed06da3a385600979380a03b7f0fe1c6ef25470ecd6a1fa9478910465","observation_id":"f4f275cd-d7b4-46a8-a3ed-bfce40014f1b","resolution":{"observed_at":"2026-05-25T12:36:58.065724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6211","last_updated":"2015-03-04T01:43:31Z","snapshot_observed_at":"2026-07-06T03:31:33.797310Z","submitted_at":"2013-12-21T06:31:41Z","title":"An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks","version":3},"cited_work":{"arxiv_id":"1312.6211","doi":"10.48550/arxiv.1312.6211","metadata_source":"pith","pith_arxiv_id":"1312.6211","snapshot_observed_at":"2026-07-10T12:07:03.480400Z","title":"An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks","venue":"stat.ML","work_id":"2d7055f4-b7d2-4ddb-a9c7-481bd728d360","year":2013},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1312.6211","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:630b61b9fbf3d738ededb007cedb23dadd7442c18235d85f89c16b6495a0a803","observation_id":"0d3f0c16-5b79-4557-aa21-33d7be9d1186","resolution":{"observed_at":"2026-05-25T12:36:57.665682Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning both weights and connections for efficient neural network","venue":null,"work_id":"49bcdc66-3747-4270-bee8-b9c25d5ebae6","year":2015},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:fdb034d188c31ab5c305e1cf26334b66161d0979d049fa2b2a16369fb60759f4","observation_id":"78818911-2d26-4170-8e08-1890bbe11086","resolution":{"observed_at":"2026-05-25T12:36:57.974535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Second order derivatives for network pruning: Optimal brain surgeon","venue":null,"work_id":"6eae03d1-8ccc-48d2-aef3-eefc09064b43","year":1993},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:129da230597918752883a76e654c33dd4bc188b0c77176797149454b3d08a424","observation_id":"e24f9d3e-2970-4955-89b7-8c193be17ef9","resolution":{"observed_at":"2026-05-25T12:36:57.990678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mask r-cnn","venue":null,"work_id":"4ac4b62a-e137-42c3-9895-5ac60c126b4a","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:7938442a9fc9223fd3801062edfd44a108ca31b045823a5e8c712472cfd09415","observation_id":"9b9de255-05b3-44a9-a950-d3b275d12643","resolution":{"observed_at":"2026-05-25T12:36:57.935858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"7d1d604f-bc89-483e-8171-20d4f5f25133","year":2016},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:31019e4a7394be5cfccd4578d06e0c81788170e6ef1a7c65a401e9cee8ad33f4","observation_id":"5ff7e998-60f7-43c4-9365-77f3b0f9a193","resolution":{"observed_at":"2026-05-25T12:36:57.938889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":"1503.02531","doi":"10.1109/cvpr52733.2024.01515","metadata_source":"pith","pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Distilling the Knowledge in a Neural Network","venue":"stat.ML","work_id":"d927ab1f-17b8-4002-9d09-c3d55764fbad","year":2015},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:2450752314ae54822931f6f6e3b932aa067f71b7ea152578dc50e20129d985b5","observation_id":"17102948-cd7b-4563-af4c-f93debffddad","resolution":{"observed_at":"2026-05-25T12:36:57.661441Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multi-scale dense net- works for resource efficient image classification","venue":null,"work_id":"bea436c4-35ef-40ba-a9f7-8079865f95b4","year":2018},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:d5c2d818d7c8e976f3054d43f7d1d8fdd35b29e0890f782dbd11f7c7613b9515","observation_id":"9954ff36-3273-436f-b294-01df7f0be6bd","resolution":{"observed_at":"2026-05-25T12:36:57.943679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multi-task learning using uncertainty to weigh losses for scene geometry and seman- tics","venue":null,"work_id":"c184ccc9-e535-4543-a84d-7f0180d26a98","year":2018},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:51dbf8232617f7a92d2a1c42e34288e6d4b8c45fd050fdd455318b33f25c07d5","observation_id":"a02de8bc-533b-40bb-bd74-c00dde530b99","resolution":{"observed_at":"2026-05-25T12:36:57.996815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Overcoming catastrophic forgetting in neural networks.National Academy of Sciences","venue":null,"work_id":"75f51e74-7a38-4723-859f-8d02f5c9d668","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:ef0d006cc003246624d97db30afb9dca3a29b454646397f2610b4c6cbb74f4fa","observation_id":"8e80b8c1-6fc4-49f9-b019-5cd3b0eedb2e","resolution":{"observed_at":"2026-05-25T12:36:57.924403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"3D object representations for fine-grained categorization","venue":null,"work_id":"c158c708-3f6d-4efa-8019-c00ff5d22b57","year":2013},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:56279ada7d27d1aad163ad1165f4c3c89b0fb5e602ba3d3757cac7088a1210f4","observation_id":"9d621d1d-7215-47ff-8677-9caf7168cfa6","resolution":{"observed_at":"2026-05-25T12:36:57.930532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":"d2f011ac-3d26-4a9d-bee8-f56d2dc461b3","year":2009},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:62a25a95651fc02be437a5e89ae1a52f4d6e02a107ddc3cfa551023f2034cff6","observation_id":"74297234-1285-4848-8cb3-3bc461668e5b","resolution":{"observed_at":"2026-05-25T12:36:57.933256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Im- agenet classification with deep convolutional neural networks","venue":null,"work_id":"52083e3b-c613-4f30-bda8-eb343a9c34d4","year":2012},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:c8cace782c1cf229c63ca8d3717eca57a92959a705ca5254f4d3d8634f8e8a70","observation_id":"0461661b-9f62-4a1b-b61a-70d0d2916214","resolution":{"observed_at":"2026-05-25T12:36:58.006134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Human-level concept learning through probabilistic program induction","venue":null,"work_id":"c121135b-b4f1-4c25-ba48-15ff91f120ac","year":2015},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:1f8271b070d387c893ca6522507aad88a8093f0678bcbf13b95bb8731bcd5251","observation_id":"7ad22ce2-0187-472c-8df1-0b691a6ea317","resolution":{"observed_at":"2026-05-25T12:36:58.058203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Optimal brain damage","venue":null,"work_id":"b2d0fa24-1ef8-43cd-9941-c746be893d7c","year":1990},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:cb6fa46dc2bc91843509790bbfe83f998cd783b123e900cf6e05e1a7aa4e1d21","observation_id":"395b9a52-9519-4af8-861e-f3d6d36e18b6","resolution":{"observed_at":"2026-05-25T12:36:58.050354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Overcoming catastrophic forgetting by incremental moment matching","venue":null,"work_id":"ee943b5c-d1bb-4054-bdf9-5d0a020f820a","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:465f085ed4d68177269d752e541a30fba4d728b38ffcdc69f3f67f67d0c1bd5c","observation_id":"c47d7c10-fdb4-4ceb-b315-54b988c04642","resolution":{"observed_at":"2026-05-25T12:36:58.088950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1608.08710","last_updated":"2017-03-10T17:57:56Z","snapshot_observed_at":"2026-07-06T05:08:42.861486Z","submitted_at":"2016-08-31T02:29:59Z","title":"Pruning Filters for Efficient ConvNets","version":3},"cited_work":{"arxiv_id":"1608.08710","doi":"10.48550/arxiv.1608.08710","metadata_source":"pith","pith_arxiv_id":"1608.08710","snapshot_observed_at":"2026-07-11T01:07:44.623701Z","title":"Pruning Filters for Efficient ConvNets","venue":"cs.CV","work_id":"66e4e995-929b-4659-8ff6-5d0b96248535","year":2016},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1608.08710","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:5bf8848c7ad92c0cbb4ce36adfb9011741f3cd14c4327343d1fb9f703239f186","observation_id":"eea1ef0c-e98d-4ab4-8c04-71cc48223284","resolution":{"observed_at":"2026-05-25T12:36:57.675177Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning without forgetting","venue":null,"work_id":"46451deb-ab17-4a87-b18e-6961d22cc4e3","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:19baa0be36ef3c22f320d91166832a27bbfa8c9b68f673e12fbf34c4509c319a","observation_id":"5546339f-b0e9-49c4-a0ac-9cb73ac48f4e","resolution":{"observed_at":"2026-05-25T12:36:58.085747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Microsoft coco: Common objects in context","venue":null,"work_id":"c9afa549-0b83-4c3b-9835-894e69af480d","year":2014},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:c5125a8d4f9a45043ed88f20791d005a4451b0ba007fccceef36488573dbe06a","observation_id":"80c1e539-fdf5-47ac-8431-0a4d7f898de2","resolution":{"observed_at":"2026-05-25T12:36:58.009754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Progressive neural architecture search","venue":null,"work_id":"14aa82b9-c60d-467a-9dfe-c15f3ae1f208","year":2018},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:8d241a4cb27a2d945954c5738c29ae50e2d09551d200e493e5a2609f9da7bc8a","observation_id":"625fa44d-d99d-41ff-b0b2-fdb135583214","resolution":{"observed_at":"2026-05-25T12:36:58.002796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.09055","last_updated":"2019-04-23T06:29:32Z","snapshot_observed_at":"2026-08-01T17:05:53.501765Z","submitted_at":"2018-06-24T00:06:13Z","title":"DARTS: Differentiable Architecture Search","version":2},"cited_work":{"arxiv_id":"1806.09055","doi":null,"metadata_source":"pith","pith_arxiv_id":"1806.09055","snapshot_observed_at":"2026-07-10T21:27:35.708872Z","title":"DARTS: Differentiable Architecture Search","venue":"cs.LG","work_id":"2f7f6e44-42e7-498e-a68f-a01e092a8903","year":2018},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1806.09055","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:9cc0e4c398a74e8a837eabf0ead10bc2c7c0e95258d0ecf8531618803746bc6b","observation_id":"bcd1562e-2b05-48c5-8d75-f1223478919e","resolution":{"observed_at":"2026-05-25T12:36:57.698616Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Gradient episodic memory for continual learning","venue":null,"work_id":"0bbf9a11-04f4-4413-a3dd-e99dc5e5d715","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:7523d0354c5a42e760f339ff7c8befdfdb16455a956c7ca7b7e9e7068e1bb3fc","observation_id":"941beeff-faa3-4448-af03-f62837d84459","resolution":{"observed_at":"2026-05-25T12:36:57.953469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1306.5151","last_updated":"2013-06-21T14:31:57Z","snapshot_observed_at":"2026-07-06T03:16:28.287173Z","submitted_at":"2013-06-21T14:31:57Z","title":"Fine-Grained Visual Classification of Aircraft","version":1},"cited_work":{"arxiv_id":"1306.5151","doi":null,"metadata_source":"pith","pith_arxiv_id":"1306.5151","snapshot_observed_at":"2026-07-11T03:07:53.092922Z","title":"Fine-Grained Visual Classification of Aircraft","venue":"cs.CV","work_id":"ed360110-3ce4-4959-8c74-1785cd9e537d","year":2013},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1306.5151","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:13729fa918e003456a2c4ad58095f3fe50148e0f3dcbac5cefec523e32bea473","observation_id":"9e5e2e39-c9f1-4be0-9078-5673befd0f3b","resolution":{"observed_at":"2026-05-25T12:36:57.649521Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Piggyback: Adapting a single network to multiple tasks by learning to mask weights","venue":null,"work_id":"898b4f5a-f4d4-4c86-89d7-a8988adee4f2","year":2018},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:d39092f18ec48fa05ecb6951ace86c8a28a413697c0eecac5f25469da2e4a121","observation_id":"4ba5f8dc-d452-4ee7-88d8-d16611467873","resolution":{"observed_at":"2026-05-25T12:36:57.964423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Packnet: Adding multiple tasks to a single network by iterative pruning","venue":null,"work_id":"da203a09-fa50-47d0-947b-9a815c52a3e5","year":2018},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:fbc474a37f72b3bb51e7bbcbbffcbfc3170e38b0aab1c60d0f66fe48abb6f71d","observation_id":"3ecd3cf1-c21b-46fe-88af-13e37aa034e5","resolution":{"observed_at":"2026-05-25T12:36:57.971572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.00183","last_updated":"2017-11-29T20:57:09Z","snapshot_observed_at":"2026-07-06T05:49:21.582343Z","submitted_at":"2017-07-01T18:13:17Z","title":"Teacher-Student Curriculum Learning","version":2},"cited_work":{"arxiv_id":"1707.00183","doi":null,"metadata_source":"pith","pith_arxiv_id":"1707.00183","snapshot_observed_at":"2026-06-28T23:52:49.216887Z","title":"Teacher-Student Curriculum Learning","venue":"cs.LG","work_id":"7c8c807e-3c64-4652-92a5-0bfcbb669352","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1707.00183","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:e71e3d3c3c140a16fdbe236ea16c51d9475ee9075f6facbf1ba6107acdbf54b2","observation_id":"adc94403-5809-4759-b43a-bd17d5056bdc","resolution":{"observed_at":"2026-05-25T12:36:57.653622Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cross-stitch networks for multi-task learning","venue":null,"work_id":"2b9f6fb5-f922-41af-96eb-2f03db83352a","year":2016},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:af57d69ec86f105d1cfa7c1978c3616506807c4850eb89ae833410b8b4ece8ba","observation_id":"42a96e4f-adf7-4d51-9838-c5530146a29e","resolution":{"observed_at":"2026-05-25T12:36:57.977699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Never-ending learning","venue":null,"work_id":"2f47f8a7-1fa9-4c09-99ea-1f1acb1d61df","year":2018},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:bb320b918ed38278a3c3abbbf6b5e72185821797264047b7ff13cbeb322b45e8","observation_id":"95c28258-4129-4e46-9d09-8a165bae5bd7","resolution":{"observed_at":"2026-05-25T12:36:57.920672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.06440","last_updated":"2017-06-08T19:53:26Z","snapshot_observed_at":"2026-07-06T05:19:21.142307Z","submitted_at":"2016-11-19T22:48:30Z","title":"Pruning Convolutional Neural Networks for Resource Efficient Inference","version":2},"cited_work":{"arxiv_id":"1611.06440","doi":"10.48550/arxiv.1611.06440","metadata_source":"pith","pith_arxiv_id":"1611.06440","snapshot_observed_at":"2026-07-11T01:07:44.986330Z","title":"Pruning Convolutional Neural Networks for Resource Efficient Inference","venue":"cs.LG","work_id":"0da622ef-0141-4ece-b202-eab31556f979","year":2016},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1611.06440","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:d5a76fc29fb33ad5dc20fdc53ce03e370afc1f049405f47bbcba75a2c5ed1026","observation_id":"929634ee-009a-4c2f-a8f5-47aa1a0d10fe","resolution":{"observed_at":"2026-05-25T12:36:57.682221Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"An experimental study on pedestrian classification.IEEE Transactions on P attern Analysis and Machine Intelligence (TP AMI), 28(11):1863–1868","venue":null,"work_id":"b256b4b7-1ff8-4fe1-8e97-e1ef250de730","year":2006},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:1584f0b009ad1ce769ab9f6eaaddb2d7190143c5fe75fee35330164360474d63","observation_id":"2d0d1c6b-87ef-4aa5-8598-b56529896d0c","resolution":{"observed_at":"2026-05-25T12:36:57.959287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Reading digits in natural images with unsupervised feature learning","venue":null,"work_id":"b671f41d-c821-4e7c-803a-17093de8ff47","year":2011},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:20932ac0fe5c4cc88510626304bdb81c6f1446d8092434f199b11b3dd6a0de9d","observation_id":"7aa371b2-16d1-4bf9-8b2f-84c1f48746e1","resolution":{"observed_at":"2026-05-25T12:36:58.072333Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Nilsback and A","venue":null,"work_id":"11629e0f-f4c5-4b0c-8296-2486d6a75365","year":2008},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:25c50967de173344c269461d8c1a6d8ba44d4a7bf1667496592275e24d385337","observation_id":"4b702c5a-8fd4-467d-bbfe-66344e8bab91","resolution":{"observed_at":"2026-05-25T12:36:57.987718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition","venue":null,"work_id":"5364bc78-c79b-43f0-b81a-4bbf40e56582","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:739adfc77d5ccf5075cad48aa43867ceb6d0ff4c9f06625ad628f4ffedaefffd","observation_id":"256f44cf-bc21-44a2-a1aa-efecb3a20318","resolution":{"observed_at":"2026-05-25T12:36:57.927452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Encoder based lifelong learning","venue":null,"work_id":"9e502fe3-7144-4471-80be-f762eebf0cbb","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:9bdc044ef110ca0b098d2c11fe4d201ff1c79ac733cd98a3f46f81b27215b48c","observation_id":"120fdc55-62cd-4a75-a49d-908905efea74","resolution":{"observed_at":"2026-05-25T12:36:58.040519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning multiple visual domains with residual adapters","venue":null,"work_id":"d6309f5e-47b6-4d71-8d13-f90d3ad4c9bf","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:83416c689c79010ad9568d2335f9cda2be3322316fa36cec9521811a23e275a4","observation_id":"1a2effc8-e03c-4c57-8bbb-92b1b6bb00a4","resolution":{"observed_at":"2026-05-25T12:36:58.075477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Efficient parametrization of multi-domain deep neural networks","venue":null,"work_id":"6717e1dc-5e36-4679-ae86-ef07d1a67d3d","year":2018},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:899c14bba186254ac73d547d2a640478e669ab591598efd9c8f8b985617b538d","observation_id":"cbc06585-8933-4450-a4c6-fa43e00ee7e2","resolution":{"observed_at":"2026-05-25T12:36:57.917502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"icarl: Incremental classifier and representation learning","venue":null,"work_id":"4e48ca17-fc16-44ec-b6bd-511d9856f825","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:87e3fe730f3f09c1452527d0df1f4447c9391705472e303534b6be1c30008c53","observation_id":"f5b153ae-58b2-474c-a6c4-9f4f609ba53f","resolution":{"observed_at":"2026-05-25T12:36:57.950558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Y ou only look once: Unified, real-time object detection","venue":null,"work_id":"b44a6239-546b-40c3-bb64-17b2bffde278","year":2016},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:771e1f56762e4eedd7dcb4c0db2d5c06cea1ac6ab5f7fb10dbc97d88ece1fe31","observation_id":"25f9d0a6-f9dc-4bbf-9f0c-83d94073f7d1","resolution":{"observed_at":"2026-05-25T12:36:57.967852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6550","last_updated":"2015-03-27T11:52:28Z","snapshot_observed_at":"2026-07-06T04:04:16.777653Z","submitted_at":"2014-12-19T22:40:51Z","title":"FitNets: Hints for Thin Deep Nets","version":4},"cited_work":{"arxiv_id":"1412.6550","doi":"10.48550/arxiv.1412.6550","metadata_source":"pith","pith_arxiv_id":"1412.6550","snapshot_observed_at":"2026-07-10T21:17:35.904852Z","title":"FitNets: Hints for Thin Deep Nets","venue":"cs.LG","work_id":"057e16e2-a9fd-4a16-8fa3-8806ac7d3165","year":2014},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1412.6550","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:87aec44b703f1c2d6ef3f70176559f98e34d584952ecc43a38ce10c10a709135","observation_id":"ac3f63dc-174a-4ae7-9c95-e103a3efd633","resolution":{"observed_at":"2026-05-25T12:36:57.669539Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:21.382784+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:21.382784+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Incremental learning through deep adaptation.IEEE Transactions on P attern Analysis and Machine Intelligence (P AMI)","venue":null,"work_id":"94ffa3f5-849d-4cf8-b6f0-3455b55f658a","year":2018},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:3512feecbac8e93822588be2d6a6748416eeccc26226d3de30116c8f2c67d5d1","observation_id":"398118dd-c2e5-45ce-9308-91e4dc22d64a","resolution":{"observed_at":"2026-05-25T12:36:58.029755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.04671","last_updated":"2022-10-22T14:34:44Z","snapshot_observed_at":"2026-07-06T05:00:01.744710Z","submitted_at":"2016-06-15T08:20:51Z","title":"Progressive Neural Networks","version":4},"cited_work":{"arxiv_id":"1606.04671","doi":"10.1007/978-3-030-58598-3_10","metadata_source":"pith","pith_arxiv_id":"1606.04671","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Progressive Neural Networks","venue":"cs.LG","work_id":"0700d73f-b94d-4cd3-be40-086e4c4544c4","year":2016},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1606.04671","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:4cf51aea3ba246db2821c19d5a847a5f9609b6b2b549bddb1e7cce9d201fd6d6","observation_id":"ab6b5329-f721-4356-b85a-9e777b471203","resolution":{"observed_at":"2026-05-25T12:36:57.644513Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1505.00855","last_updated":"2015-05-05T01:25:26Z","snapshot_observed_at":"2026-07-06T04:16:54.272660Z","submitted_at":"2015-05-05T01:25:26Z","title":"Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature","version":1},"cited_work":{"arxiv_id":"1505.00855","doi":null,"metadata_source":"pith","pith_arxiv_id":"1505.00855","snapshot_observed_at":"2026-07-10T15:47:23.327780Z","title":"Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature","venue":"cs.CV","work_id":"6c4ab46a-341c-4ec0-a466-0ef53934ec11","year":2015},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1505.00855","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:555b87a88f622a2a84be592de86fe392075458cc669040b704f404729b78aaec","observation_id":"002ef82a-8647-4c1a-ae1d-b6bb3c62ba25","resolution":{"observed_at":"2026-05-25T12:36:57.640268Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Facenet: A unified embedding for face recognition and clustering","venue":null,"work_id":"01fa54e9-ae41-48d1-8879-ac6f94575666","year":2015},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:ec2ab10178f54eb731e302f8de3f9ba1b8db06d39aaa74623a32d5b485784f3b","observation_id":"48d479b4-4b46-4ae0-9697-cc7ae6e2c0a9","resolution":{"observed_at":"2026-05-25T12:36:57.914402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cnn features off-the-shelf: an astounding baseline for recognition","venue":null,"work_id":"10a83462-fb78-40bc-aec7-e480e702d7db","year":2014},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:2f280fc423b09f9735a8270abda2a555775c741854566027733469f58316b178","observation_id":"c6406c48-441c-4626-8924-695c95459eec","resolution":{"observed_at":"2026-05-25T12:36:58.054996Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1212.0402","last_updated":"2012-12-03T14:45:31Z","snapshot_observed_at":"2026-07-06T03:01:10.229407Z","submitted_at":"2012-12-03T14:45:31Z","title":"UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild","version":1},"cited_work":{"arxiv_id":"1212.0402","doi":"10.48550/arxiv.1212.0402","metadata_source":"pith","pith_arxiv_id":"1212.0402","snapshot_observed_at":"2026-07-11T03:07:53.363433Z","title":"UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild","venue":"cs.CV","work_id":"5dfb46e7-e952-409d-a3c7-ba7f20aebad6","year":2012},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1212.0402","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:7c871d27947d64b354a2b892886054e8f7c1ddd3d8e9ae890e2ea51e93d846be","observation_id":"44431afd-61e0-4962-863a-cc18e862076c","resolution":{"observed_at":"2026-05-25T12:36:57.686434Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"07925f61-cfe5-41bb-92c8-e9707cd97048","year":2012},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:730fb35b4892015ded18742d72bc19040ac6a4c6d145f8c60e792a3300db86fa","observation_id":"f33dafb1-dda1-4dd0-844c-0800d2c31a44","resolution":{"observed_at":"2026-05-25T12:36:57.993408Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Branchynet: Fast inference via early exiting from deep neural networks","venue":null,"work_id":"8120f0f6-0231-4222-b418-31157ade4dcd","year":2016},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:28b27a176152bee81ace26136f8048193cb4e0ec7dacc08974eb437b1e96eb9c","observation_id":"2d746eff-d01a-401c-9ed9-54af29196c25","resolution":{"observed_at":"2026-05-25T12:36:57.984438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A lifelong learning perspective for mobile robot control","venue":null,"work_id":"08a15a62-52a6-4819-9588-c9c31e6cbe5e","year":1995},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:cddc6cd1a3fbf8b60164e299eb3a4ef2556d35f200edb04325814d66bd9c7e5e","observation_id":"2f059af4-ad1c-48a5-917f-4bca638392e4","resolution":{"observed_at":"2026-05-25T12:36:58.096416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Simultaneous deep transfer across domains and tasks","venue":null,"work_id":"75184bd6-f82b-407f-9409-3a7a756439b9","year":2015},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:aa42f1b1484128ee72df129fe5451cef166f9d936d510796302dae1ffeab5b93","observation_id":"1034914a-22e5-4df8-943f-421de4186c96","resolution":{"observed_at":"2026-05-25T12:36:58.082255Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Adversarial discriminative domain adaptation","venue":null,"work_id":"4e027041-1d65-4c44-bf3c-4f3abf13ac74","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:787dffcae0c22c7a11b0bb396c46d378c7a48a5c7000791a292e83fabb980bc0","observation_id":"0238a744-22b7-4710-8427-cbfd1530c444","resolution":{"observed_at":"2026-05-25T12:36:58.061976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Convolutional networks with adaptive inference graphs","venue":null,"work_id":"ff557357-f3aa-4b19-9c4e-c1b814f22b80","year":2018},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:5088dd4eb48ea55485faa66473f13d2eb334419606ed83056958c75239a45121","observation_id":"e567e426-73f9-4048-9c56-60156a7d13dd","resolution":{"observed_at":"2026-05-25T12:36:58.079182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Rapid object detection using a boosted cascade of simple features","venue":null,"work_id":"b8208b37-7ea6-4085-b750-da43797f8241","year":2001},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:6575eefcf338f62fcd02632ee264cc713f39e02dbeb8d2d2528a4b50dc5f4e88","observation_id":"1e23c72c-430b-4c82-b71d-3fbb0785e33c","resolution":{"observed_at":"2026-05-25T12:36:58.101154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"077f2f88-4b61-4193-8c81-a4821d106293","year":2011},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:d77bf8e14b1df3b6c8b434e029ce64d958ffc48c0cfa4b4c901b1ee91f3f81bc","observation_id":"14953e0c-2100-4deb-9d88-88c95ed2f33c","resolution":{"observed_at":"2026-05-25T12:36:58.043524Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Exploit all the layers: Fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers","venue":null,"work_id":"b44d58d6-40f1-46b1-ba23-5d4b657b6744","year":2016},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:39b7ddeda3b1b8fa63790c5f3ed9a369774e4729cf6397bb27343c2693333c32","observation_id":"6b8f01ef-8733-4d3a-b76d-1c95d808bc7b","resolution":{"observed_at":"2026-05-25T12:36:58.037525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.01547","last_updated":"2018-06-11T09:03:44Z","snapshot_observed_at":"2026-07-06T05:54:07.143205Z","submitted_at":"2017-08-04T15:14:31Z","title":"Lifelong Learning with Dynamically Expandable Networks","version":11},"cited_work":{"arxiv_id":"1708.01547","doi":null,"metadata_source":"pith","pith_arxiv_id":"1708.01547","snapshot_observed_at":"2026-07-04T06:19:38.006180Z","title":"Lifelong Learning with Dynamically Expandable Networks","venue":"cs.LG","work_id":"e6bf3c15-5317-49f7-b85c-e533d387a9cc","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1708.01547","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:0de9709cb53affe382b6cfb1ea12d25ea3733ed51ecde949e61c4a91198a965f","observation_id":"dc7930ec-e4d9-4344-b197-afe1d9b6d90c","resolution":{"observed_at":"2026-05-25T12:36:57.694835Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"How transferable are features in deep neural networks? InAdvances in Neural Information Processing Systems (NeurIPS)","venue":null,"work_id":"50882498-6de8-41f5-840e-82c631b334b9","year":2014},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:5ffeae05afdd0220d247dbbce142271923161f447ae4e3c353b8a66b32b68c63","observation_id":"df47a911-4b36-4910-ac50-c0d9dc543257","resolution":{"observed_at":"2026-05-25T12:36:58.034162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Wide residual networks","venue":null,"work_id":"febf9687-2169-4000-a9c9-a36f186b79b6","year":2016},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:e5972184d620f3f306d343d5d5c42afb88e0309b8c1fca506cf35315ec3d17dd","observation_id":"63efa0dc-a185-4b8e-8b1f-d1bfb051c4b7","resolution":{"observed_at":"2026-05-25T12:36:58.015943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Facial landmark detection by deep multi-task learning","venue":null,"work_id":"9b0163b9-ca79-4b72-ac2e-23241411087e","year":2014},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:49a847b7e2664859e58dde57228c8e961a16e446a7db6b9d5d0d80a04487cd08","observation_id":"e0045f71-47c5-4f50-85ce-175265496a03","resolution":{"observed_at":"2026-05-25T12:36:58.019583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning deep features for scene recognition using places database","venue":null,"work_id":"96ca4c42-b430-4d8d-b343-4e26a74a40b9","year":2014},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:9b044e51a51f2fb30a4f77f4cd49cc47f839644e0b6fc0a52afc98f4573a6e71","observation_id":"c7547a1e-fae2-4b3a-82e3-a17c77ffb53b","resolution":{"observed_at":"2026-05-25T12:36:58.025938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Less is more: T owards compact cnns","venue":null,"work_id":"a8282b86-c467-445f-81fc-b8f86d4e4810","year":2016},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:cbcdd8299ecaf86c0ad997fdec24290cfb0df63f516a63d338d023161fecb6bb","observation_id":"de1486fb-49ef-41ac-8f06-d063e5ffab61","resolution":{"observed_at":"2026-05-25T12:36:58.012969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.01578","last_updated":"2017-02-15T05:28:05Z","snapshot_observed_at":"2026-07-06T05:17:29.499249Z","submitted_at":"2016-11-05T00:41:37Z","title":"Neural Architecture Search with Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"1611.01578","doi":"10.1016/j.knosys.2015.01.010","metadata_source":"pith","pith_arxiv_id":"1611.01578","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Neural Architecture Search with Reinforcement Learning","venue":"cs.LG","work_id":"376934b8-2ad9-4443-87fe-ff630c1b89d9","year":2016},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1611.01578","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:ec7b47b7c19fd698e4ea78dd67a801d93ca97163718b7280a7be850373a9c23b","observation_id":"a3ef7730-0c81-453c-9240-a0e67e0477da","resolution":{"observed_at":"2026-05-25T12:36:57.631634Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.07012","last_updated":"2018-04-11T05:12:21Z","snapshot_observed_at":"2026-07-06T05:52:12.669629Z","submitted_at":"2017-07-21T18:10:26Z","title":"Learning Transferable Architectures for Scalable Image Recognition","version":4},"cited_work":{"arxiv_id":"1707.07012","doi":null,"metadata_source":"pith","pith_arxiv_id":"1707.07012","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning Transferable Architectures for Scalable Image Recognition","venue":"cs.CV","work_id":"2ad703e7-0f85-4ba6-aefa-145dac3beecd","year":2017},"citing_paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-05-25T12:36:34.595988Z"},"links":{"cited_paper":"/paper/1707.07012","citing_paper":"/paper/1907.00274"},"observation_digest":"sha256:f93d6f7b6cf0b79179c2afcf0e6c76e341629945eb46dacf8db00b08ab3f369b","observation_id":"7d86da89-f95f-484e-a667-8a53437abf25","resolution":{"observed_at":"2026-05-25T12:36:57.636021Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1907.00274","last_updated":"2019-06-29T20:32:58Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T08:03:47.885341Z","submitted_at":"2019-06-29T20:32:58Z","title":"NetTailor: Tuning the Architecture, Not Just the Weights"},"reference_resolution":{"displayed":76,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":16,"verified_fuzzy":58},"total_outbound_references":76},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 0 inbound Pith citation observations for arXiv:1907.00274."}