{"as_of":"2026-08-13T10:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b0db0a741c0de6001d577433aca4cb176eabce60d8f3bf2a15822e9464ce6e48","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T19:02:07.166109Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T12:50:30.719164Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-10T22:49:40.526907Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.11079","snapshot_observed_at":"2026-08-12T12:50:30.719164Z","title":"Electrostatic force regularization for neural structured pruning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16901","last_updated":"2024-11-25T20:10:10Z","snapshot_observed_at":"2026-08-12T12:43:20.985421Z","submitted_at":"2024-11-25T20:10:10Z","title":"Deep Convolutional Neural Networks Structured Pruning via Gravity Regularization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T12:50:30.719164Z"},"links":{"cited_paper":"/paper/2411.11079","citing_paper":"/paper/2411.16901"},"observation_digest":"sha256:606c7ed7cfbf0c8266b091b0c14df068ff37b74f9d34b154476dd30613cdf094","observation_id":"c26a3c3f-8dcd-4936-baf0-f88fe9b2dd14","resolution":{"observed_at":"2026-08-12T12:50:30.719164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"cited_work":{"arxiv_id":"2411.11079","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.11079","snapshot_observed_at":"2026-08-10T22:49:40.526907Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","venue":"cs.CV","work_id":"63c0b0be-8edb-4dab-90aa-2e0292a3e664","year":2024},"citing_paper":{"arxiv_id":"2501.00647","last_updated":"2024-12-31T21:07:40Z","snapshot_observed_at":"2026-08-13T06:32:52.946634Z","submitted_at":"2024-12-31T21:07:40Z","title":"Lightweight G-YOLOv11: Advancing Efficient Fracture Detection in Pediatric Wrist X-rays","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T22:49:40.419446Z"},"links":{"cited_paper":"/paper/2411.11079","citing_paper":"/paper/2501.00647"},"observation_digest":"sha256:9c781c9c0cc3eeaec61d6fedf14b02c56c5f344c145ba50c1c25ed8b1359e3cf","observation_id":"80a543d7-1bae-4c28-b1ba-8e3b12fa7724","resolution":{"observed_at":"2026-08-10T22:49:40.535602Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.11079/citation-record","integrity":"/paper/2411.11079/integrity","json":"/paper/2411.11079/citation-record.json","paper":"/paper/2411.11079"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.616456Z","title":"Restructuring the teacher and student in self-distillation","venue":null,"work_id":"76de4857-bdd3-4c18-b4eb-b1a8100541b8","year":2024},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.015153Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:dfcdf5c1ec851285a4973573a8a693ea1b63910701dedb3afbbc422ca68e973a","observation_id":"16ed0cad-f0e3-4585-a503-3a489ddc7227","resolution":{"observed_at":"2026-08-12T19:02:07.620401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.604737Z","title":"Low-rank approximation for sparse attention in multi-modal llms","venue":null,"work_id":"a88c164a-024f-474f-9c17-8b55b9688612","year":2024},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.019505Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:5c98a5028d5cdaea47298900f0cade634e9fccf42095b4a6deab705eafe99c50","observation_id":"8b1b10c3-82d7-4a2d-a5e4-00a7ad095fb0","resolution":{"observed_at":"2026-08-12T19:02:07.608859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.023464Z","title":"Quantization via distillation and contrastive learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.023464Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:ea35b2d2b9555c09bb1bec3047edf72529c5da97c17813c78e83eb1f8ce1593e","observation_id":"be6bcba9-de59-4ba5-9ded-b4a7d72cab78","resolution":{"observed_at":"2026-08-12T19:02:07.023464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.585738Z","title":"Discrimination-aware network pruning for deep model compression","venue":null,"work_id":"6d02c7b0-afd0-4fca-b919-3c01057d6045","year":2021},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.027371Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:52221c4ab5f627c5d5df1245bb5692f89d06951ea31d2c2459bdb9fb7360f86e","observation_id":"9f5e2f25-15ae-408b-a024-1c98bdfa4f12","resolution":{"observed_at":"2026-08-12T19:02:07.589674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.573877Z","title":"Complexity-driven model compression for resource- constrained deep learning on edge","venue":null,"work_id":"82a457f9-e4eb-4e7a-b704-e4ba9e137915","year":2024},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.031814Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:deb91a43caad1fabd15f9041bcc970fa965b9e4bdd42e2d38d579c6c869863f7","observation_id":"8d51df25-c577-48c6-b83d-04b13658e9f2","resolution":{"observed_at":"2026-08-12T19:02:07.577800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.560985Z","title":"Ganji, Ivan Lazarevich, and Sudhakar Sah","venue":null,"work_id":"8fd34de5-3771-4095-a738-d152e234edd4","year":2023},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.035966Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:3642883624eb88968db4cca55ce48a893830e44184098a57645a36eed3325050","observation_id":"23a2a498-784f-431f-9221-fd4af53e62d6","resolution":{"observed_at":"2026-08-12T19:02:07.564928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.549379Z","title":"Advancing model pruning via bi-level optimization","venue":null,"work_id":"5ae8cab8-f526-45ba-93dd-a8ff0d0e6c5c","year":2022},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.040154Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:0c94df52c07587ccb576d722b5deb7c43c1d40ecd9c3bf002b4199a43d20eaa2","observation_id":"256ca393-65a0-412f-b1d6-1a3e54b521f6","resolution":{"observed_at":"2026-08-12T19:02:07.553358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.537997Z","title":"Prior gradient mask guided pruning-aware fine-tuning","venue":null,"work_id":"76098a89-4d6f-43a0-9bf1-47d101d15003","year":2022},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.043728Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:744d8dd6b9d4d4223f30a7e45d938635d899ffe7f6d854d9b01b5e323454cc4f","observation_id":"96f148d5-3ebe-4bd1-9c52-0d43edfc7a45","resolution":{"observed_at":"2026-08-12T19:02:07.541794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.047349Z","title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.047349Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:eeb96ca053d4527240a1ff54ba8c0b687f8aadeb7d23d060335b7021f1901275","observation_id":"a91118c7-212b-4ef2-a02a-02223f8223c4","resolution":{"observed_at":"2026-08-12T19:02:07.047349Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.05270","last_updated":"2019-03-05T05:58:11Z","snapshot_observed_at":"2026-08-10T10:06:07.580743Z","submitted_at":"2018-10-11T22:15:28Z","title":"Rethinking the Value of Network Pruning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.05270","snapshot_observed_at":"2026-08-12T19:02:07.051049Z","title":"Rethinking the value of network pruning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.051049Z"},"links":{"cited_paper":"/paper/1810.05270","citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:af2fafcf17cba67fdb6b2ecefaa5145fa7ac8cef59205ff056ab3d25728ec483","observation_id":"8d25048f-4829-4109-a7ac-82c7055a005b","resolution":{"observed_at":"2026-08-12T19:02:07.051049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.518779Z","title":"Snip: Single-shot network pruning based on connection sensitivity","venue":null,"work_id":"0454e343-55d3-409d-bb3c-a680ef66d1f1","year":2018},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.055149Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:a5fde002117dc721b432fd3bdc251ab24c2033b21a1ed1b818ef322611ce93d6","observation_id":"07721258-f0f8-4a1a-87c8-d32ad57e26b9","resolution":{"observed_at":"2026-08-12T19:02:07.522977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.506855Z","title":"Progressive skeletonization: Trimming more fat from a network at initialization","venue":null,"work_id":"2c6db56b-a1db-490d-9346-e427ead0c0f2","year":2021},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.058835Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:e3697917d8fc7b62d153ee68095b7155c431f977dbaee3e0b2b190f2406d3939","observation_id":"61b6a824-5347-44f4-8b7a-67f40e9f968e","resolution":{"observed_at":"2026-08-12T19:02:07.511201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.07376","last_updated":"2020-08-07T00:02:33Z","snapshot_observed_at":"2026-08-10T04:53:00.900945Z","submitted_at":"2020-02-18T05:14:47Z","title":"Picking Winning Tickets Before Training by Preserving Gradient Flow","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.07376","snapshot_observed_at":"2026-08-12T19:02:07.063054Z","title":"Picking winning tickets before training by preserving gradient flow","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.063054Z"},"links":{"cited_paper":"/paper/2002.07376","citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:f49486541fee2e026a51199df21e6b1ef108f8495b98a8ce8ec3b6dbcb23fc7a","observation_id":"31c0cf6e-1ca9-458f-8a91-ca2b8cbc455b","resolution":{"observed_at":"2026-08-12T19:02:07.063054Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.067262Z","title":"Linear mode connectivity and the lottery ticket hypothesis","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.067262Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:29998de2141dd5294dd16cd920ec2169c1c1d86fea88a4c7dc72828d901c996e","observation_id":"59e6af9b-9918-45d4-8345-1c855ef25bc1","resolution":{"observed_at":"2026-08-12T19:02:07.067262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.487001Z","title":"Neural pruning via growing regularization","venue":null,"work_id":"f97ba805-cf99-48e3-aa17-bd32fa01c9be","year":2021},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.070975Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:dfad58bc1854e1ffffe1599966d83cf40dcc7ed11c6e41031b176621888c184b","observation_id":"955a63d9-cbe0-47c5-8949-b4060143dcea","resolution":{"observed_at":"2026-08-12T19:02:07.491914Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.473913Z","title":"Pruning parameterization with bi-level optimization for efficient semantic segmentation on the edge","venue":null,"work_id":"2d2bc824-69c4-4d27-9cf5-904d5d21262a","year":2023},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.075227Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:c588f4911b48ede843593c80f3db1fa73c913aa67f92e49c3066884fb6bef172","observation_id":"9138e827-757e-411a-b0f7-869c37158020","resolution":{"observed_at":"2026-08-12T19:02:07.479074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.461917Z","title":"Gradual channel pruning while training using feature relevance scores for convolutional neural networks","venue":null,"work_id":"8176e24d-c70e-408b-8a11-4cc432e2be73","year":2020},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.079928Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:874c5e73525cb508b25e6bb66c6147edbba8b5d546bba9ea849e7e65bd22a298","observation_id":"9a13e049-2d32-4c6d-97bc-9a26644b5ee3","resolution":{"observed_at":"2026-08-12T19:02:07.465925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.450540Z","title":"Learning filter pruning criteria for deep convolutional neural networks acceleration","venue":null,"work_id":"0f8f83e4-5aef-4c9c-b1be-c45118858b4a","year":2009},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.083416Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:ab50e4abaa8798f5ffd8ed249e1511cc5e500a9a41edf9bc3d57298534c4c85f","observation_id":"bf496c94-854b-4cc3-83a6-5be4adf5545e","resolution":{"observed_at":"2026-08-12T19:02:07.454417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.438602Z","title":"Structured compression of deep neural networks with debiased elastic group lasso","venue":null,"work_id":"c3db5c11-6547-4319-a8de-0ee6e9365018","year":2020},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.087110Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:a016c81b608b17442e4ddf98457f5484f7e1ee77f44ad9373b6e589bf7862ec6","observation_id":"84302508-a3c5-4715-b913-07407cb8c322","resolution":{"observed_at":"2026-08-12T19:02:07.442505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.427061Z","title":"Rigging the lottery: Making all tickets winners","venue":null,"work_id":"d1079f8c-438c-40cc-8563-7204306740e0","year":2020},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.091375Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:4a5cd3635ff7859b309d0cc10fd35a0f368421b8b1addb2c4dd5302c14abff06","observation_id":"275f7a2b-df6b-496b-be21-4f908676f5f7","resolution":{"observed_at":"2026-08-12T19:02:07.430976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.413646Z","title":"Efficient joint optimization of layer-adaptive weight pruning in deep neural networks","venue":null,"work_id":"1848de97-b705-4c5b-b1c5-2d1e1bb16486","year":2023},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.095722Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:c10d86702599446f172e68a2c55e4224138a0821ed4d6564c73cc4137866fbde","observation_id":"77ca09c2-a855-41b9-bec3-bb577a89cc69","resolution":{"observed_at":"2026-08-12T19:02:07.418046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.401449Z","title":"Torque based structured pruning for deep neural network","venue":null,"work_id":"1a2ec0e3-26fa-4258-a735-327486f45d1d","year":2024},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.099871Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:6ac7dfe59723a198f09f55693338eff38a86302e9d455d6549205583396b6a92","observation_id":"1a4343ef-5355-46b5-932d-fc9ef31b1735","resolution":{"observed_at":"2026-08-12T19:02:07.405225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.389761Z","title":"Orthcaps: An orthogonal capsnet with sparse attention routing and pruning","venue":null,"work_id":"16d2966a-8dcd-4d5a-a69a-ac0803b3d15d","year":2024},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.104767Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:01fc00232a68c9143f1b9d06abea3179dc72f5bf1e075d9e39e45895abcfbbc6","observation_id":"bbf59f8a-f2cb-47a6-a71d-3480457f2d6e","resolution":{"observed_at":"2026-08-12T19:02:07.393765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.375775Z","title":"Finding lottery tickets in vision models via data- driven spectral foresight pruning","venue":null,"work_id":"ab93f148-0e0e-44f9-8b51-c8305163e865","year":2024},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.108707Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:8207b903234632958d99f7187f6465b808efa2064c99d0bde11452ce9293a32b","observation_id":"ea012846-d7e6-4a02-99ea-5e7e2e7edf67","resolution":{"observed_at":"2026-08-12T19:02:07.381317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.363214Z","title":"Bilevelpruning: Unified dynamic and static channel pruning for convolutional neural networks","venue":null,"work_id":"0aa1c4b2-f471-4529-becc-fa950d5b4b50","year":2024},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.112904Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:a8023548899f9c5cdd9a9bc688c89138ab9564bebaa47cb55b614aece4c7676e","observation_id":"0d6bb1a0-4318-4073-82be-5236cfcde785","resolution":{"observed_at":"2026-08-12T19:02:07.367307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.351567Z","title":"Unipts: A unified framework for proficient post-training sparsity","venue":null,"work_id":"18805ec5-124b-4f93-be2c-48eae9c7e6a9","year":2024},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.117116Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:3a3bd7e8ef59da7c16fb81cb5cb76968b6546c0eff6ee51a231b99b08d6a0841","observation_id":"3b9b482a-ab01-4e8e-9ed2-1174cdf54ad3","resolution":{"observed_at":"2026-08-12T19:02:07.355604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.121781Z","title":"Channel pruning for accelerating very deep neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.121781Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:81646ab772dbc3d3d6fa49ac5285576b94b0128acab0cfdb2a7a4a969970f246","observation_id":"5a79af07-95d2-438e-8732-ebad2a3b1c33","resolution":{"observed_at":"2026-08-12T19:02:07.121781Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.126407Z","title":"Amc: Automl for model compression and acceleration on mobile devices","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.126407Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:c26d567a47fb7b664e33734cff05bbe14adb6a113fbb425ed09c479c18874540","observation_id":"200a1456-21db-4a8f-b72b-a239d16e47e8","resolution":{"observed_at":"2026-08-12T19:02:07.126407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.130472Z","title":"Filter pruning via geometric median for deep convolutional neural networks acceleration","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.130472Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:1905d5f7e073d5dbdef67dee619072d715a01af9f16d46cb0d78175b47039a00","observation_id":"d9b66c7a-6e2b-4dc2-acd9-51136fd37aa7","resolution":{"observed_at":"2026-08-12T19:02:07.130472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1808.06866","last_updated":"2018-08-21T12:22:38Z","snapshot_observed_at":"2026-08-10T21:04:18.551813Z","submitted_at":"2018-08-21T12:22:38Z","title":"Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1808.06866","snapshot_observed_at":"2026-08-12T19:02:07.134271Z","title":"Soft filter pruning for accelerating deep convolutional neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.134271Z"},"links":{"cited_paper":"/paper/1808.06866","citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:806882155c666f11eb925d6badf4c85601a42c93129832be46601a19cd5996ce","observation_id":"7a9bad41-f41b-411b-8722-0fff08a63d7b","resolution":{"observed_at":"2026-08-12T19:02:07.134271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.313443Z","title":"Whc: Weighted hybrid criterion for filter pruning on convolutional neural networks","venue":null,"work_id":"95c3191e-f3b7-40ab-ba6d-8ddfa57e7e61","year":2023},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.138423Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:7f2c5dec196bf2f87d351d75ababda9648c8b835dea58ab7cc52452d7bbf96ac","observation_id":"d8fd5dcd-e2f3-4b15-8fad-7c0ef02075ac","resolution":{"observed_at":"2026-08-12T19:02:07.319746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08565","last_updated":"2020-06-29T01:52:28Z","snapshot_observed_at":"2026-08-11T14:49:35.561409Z","submitted_at":"2020-01-23T14:51:19Z","title":"Channel Pruning via Automatic Structure Search","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08565","snapshot_observed_at":"2026-08-12T19:02:07.142131Z","title":"Channel pruning via automatic structure search","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.142131Z"},"links":{"cited_paper":"/paper/2001.08565","citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:795301ba5ead716557709c2ff2ae03ee5ee52b8e70cc59e782e97c3358ecf06d","observation_id":"57597a50-0700-4e9a-8c51-d49b8546f432","resolution":{"observed_at":"2026-08-12T19:02:07.142131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.300444Z","title":"Channel pruning via lookahead search guided reinforcement learning","venue":null,"work_id":"4fb08869-5496-4cef-83fa-ab9a4d12f0db","year":2022},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.146127Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:c083baf554dc5646e780baf45fcfd9f704b76698b4df83cce4e667d54aeb7893","observation_id":"9632a792-7fb4-4425-abea-55463ba8ae20","resolution":{"observed_at":"2026-08-12T19:02:07.304662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.286790Z","title":"Centripetal sgd for pruning very deep convolu- tional networks with complicated structure","venue":null,"work_id":"e239f1eb-35a9-4caa-abcc-4c4718fee113","year":2019},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.149657Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:9693d9fe8f12a49a6e3a2be292480c6c1424d478b47f77124ae3212fc8446de3","observation_id":"b81d3942-1b7d-45d7-9840-8a8d23798dc3","resolution":{"observed_at":"2026-08-12T19:02:07.291963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.269971Z","title":"Auto-balanced filter pruning for efficient convolutional neural networks","venue":null,"work_id":"6fd3a918-b345-4f92-92cf-a086004ce808","year":2018},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.153616Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:dc9e70a1a46cf31db5b51699df66db396c2ab6918bf699bdd1cd4dc5ce5abbff","observation_id":"f4be1a59-d48d-4057-9503-b9c0e2136f7d","resolution":{"observed_at":"2026-08-12T19:02:07.277319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.157307Z","title":"Eigendamage: Structured pruning in the kronecker-factored eigenbasis","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.157307Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:daccde7421a0c3650397cddc478a7b6771f8ce30b788d213943ebc3a50117163","observation_id":"cdd55650-3f83-495b-9fdc-4bd1c91278ab","resolution":{"observed_at":"2026-08-12T19:02:07.157307Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:02:07.162268Z","title":"Importance estimation for neural network pruning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.162268Z"},"links":{"citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:97fd85af578bcab602f9c2281eff8f7156dba36148f24485865d47f28d934b41","observation_id":"872151f5-fe68-4781-bea8-cdbed99cb318","resolution":{"observed_at":"2026-08-12T19:02:07.162268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1608.08710","last_updated":"2017-03-10T17:57:56Z","snapshot_observed_at":"2026-08-09T05:37:48.140401Z","submitted_at":"2016-08-31T02:29:59Z","title":"Pruning Filters for Efficient ConvNets","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1608.08710","snapshot_observed_at":"2026-08-12T19:02:07.166109Z","title":"Pruning filters for efficient convnets","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T19:02:07.166109Z"},"links":{"cited_paper":"/paper/1608.08710","citing_paper":"/paper/2411.11079"},"observation_digest":"sha256:055b09302ad0bdb33ee5e771da59a16bb4bee14a3366ce1e75d101964e6d3e3a","observation_id":"9b484c8c-38fa-4b22-b812-e475426aa591","resolution":{"observed_at":"2026-08-12T19:02:07.166109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.11079","last_updated":"2024-11-17T13:55:35Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T18:53:30.612764Z","submitted_at":"2024-11-17T13:55:35Z","title":"Electrostatic Force Regularization for Neural Structured Pruning"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":0,"verified_fuzzy":25},"total_outbound_references":38},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 2 inbound Pith citation observations for arXiv:2411.11079."}