{"as_of":"2026-08-13T16:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:61790cc246cc0fd1d1b165cfe428a3eee84a5eac97ba7f64b0966d8b33fcdc4a","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T11:08:52.079928Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2411.18578/citation-record","integrity":"/paper/2411.18578/integrity","json":"/paper/2411.18578/citation-record.json","paper":"/paper/2411.18578"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:08:52.489887Z","title":"We will use the VGG-16 architecture as the main example for implementation in this paper, but all the discussion and developed algorithms can be applied to any CNN structure","venue":null,"work_id":"9bfcbe4c-3414-4646-a92c-1456d25c20c8","year":1965},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.071039Z"},"links":{"citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:2d0ab20d0bc76d4b867587744ff9e139efb455b61bf9ca6502012d0db84732a4","observation_id":"7c61430e-ab87-49a5-83f6-bc056ef2c100","resolution":{"observed_at":"2026-08-12T11:08:52.494343Z","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":"2026-08-12T11:08:52.567849Z","title":"Eagleeye: Fast sub-net evaluation for efficient neural network pruning","venue":null,"work_id":"aba6d908-8b94-4a3e-a8c2-6f5cfc9dcf32","year":2020},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.023636Z"},"links":{"citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:299e5cc9a852bd4678c52a07b955886d3dec9743bc27d19f4933487786a466c3","observation_id":"9acd1829-9445-4836-81d9-6720c26d0c93","resolution":{"observed_at":"2026-08-12T11:08:52.573168Z","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":"1611.06440","last_updated":"2017-06-08T19:53:26Z","snapshot_observed_at":"2026-08-07T07:29:29.524604Z","submitted_at":"2016-11-19T22:48:30Z","title":"Pruning Convolutional Neural Networks for Resource Efficient Inference","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.06440","snapshot_observed_at":"2026-08-12T11:08:52.028544Z","title":"Pruning convolutional neural networks for resource efficient inference","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.028544Z"},"links":{"cited_paper":"/paper/1611.06440","citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:3cf5a56fef21529a401a0889f458c4aed4b50d0b2294aa02f2c1097545df597d","observation_id":"4efbed1c-4ace-445e-8f88-78d5bfc76962","resolution":{"observed_at":"2026-08-12T11:08:52.028544Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09994","last_updated":"2023-07-19T13:58:01Z","snapshot_observed_at":"2026-08-13T10:52:35.853023Z","submitted_at":"2023-07-19T13:58:01Z","title":"Impact of Disentanglement on Pruning Neural Networks","version":1},"cited_work":{"arxiv_id":"2307.09994","doi":null,"metadata_source":"pith","pith_arxiv_id":"2307.09994","snapshot_observed_at":"2026-08-12T11:08:52.318141Z","title":"Impact of Disentanglement on Pruning Neural Networks","venue":"cs.LG","work_id":"e9988c69-8c40-480f-8219-06162c08aba9","year":2023},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.037101Z"},"links":{"cited_paper":"/paper/2307.09994","citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:499f56cecbbbffb67eaea6d5fc3a88debf4ddd5fb5ec0dd9da504fd513a7dcb7","observation_id":"3271536f-0863-470c-aa94-bf69974faa43","resolution":{"observed_at":"2026-08-12T11:08:52.323607Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:08:52.536632Z","title":"Trained rank pruning for efficient deep neural networks","venue":null,"work_id":"5ccd206b-931d-4590-83a2-f2f0b0758a4e","year":2019},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.046374Z"},"links":{"citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:e5ab75a33bb161a598a987c89a3c2aeb8cb48150c835eaef768856fbd02cf2a7","observation_id":"c37b0a70-781d-4a42-83da-1a0b7c4b8ff2","resolution":{"observed_at":"2026-08-12T11:08:52.541784Z","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":"2026-08-12T11:08:52.457529Z","title":null,"venue":null,"work_id":"cf3b22f1-dc9b-415a-9a43-7173d8a59e71","year":null},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.079928Z"},"links":{"citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:21faa93500ac8fba0921c60c8b2c388afc87676482c78eae8ba91d1cdcb5b95c","observation_id":"201762e7-0455-4270-84a8-150f14f92df4","resolution":{"observed_at":"2026-08-12T11:08:52.462436Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"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":"2312.09411","last_updated":"2023-12-15T00:22:55Z","snapshot_observed_at":"2026-08-13T10:47:39.292952Z","submitted_at":"2023-12-15T00:22:55Z","title":"OTOv3: Automatic Architecture-Agnostic Neural Network Training and Compression from Structured Pruning to Erasing Operators","version":1},"cited_work":{"arxiv_id":"2312.09411","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.09411","snapshot_observed_at":"2026-08-12T11:08:52.442752Z","title":"OTOv3: Automatic Architecture-Agnostic Neural Network Training and Compression from Structured Pruning to Erasing Operators","venue":"cs.LG","work_id":"61d5ab13-ef51-4365-aa96-aa5b24cdad34","year":2023},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":1966,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:51.994229Z"},"links":{"cited_paper":"/paper/2312.09411","citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:476ce129699f54e244d45cb35776103b89cde6bb74f7846dc5dcf0ad70cbccfa","observation_id":"ba812eb3-3c3b-490a-bace-11fd2cf42565","resolution":{"observed_at":"2026-08-12T11:08:52.447443Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"1906.06307","last_updated":"2020-02-16T18:23:41Z","snapshot_observed_at":"2026-08-10T10:05:37.637267Z","submitted_at":"2019-06-14T17:26:29Z","title":"A Signal Propagation Perspective for Pruning Neural Networks at Initialization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.06307","snapshot_observed_at":"2026-08-12T11:08:52.018582Z","title":"A signal propagation perspective for pruning neural networks at initialization","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":1998,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.018582Z"},"links":{"cited_paper":"/paper/1906.06307","citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:4db926fdbf709cff742f238dfb638ee7032778c72b59823747dfc96aa31805c0","observation_id":"84c1bb11-555d-4917-99a4-1cfa43096ac6","resolution":{"observed_at":"2026-08-12T11:08:52.018582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04622","last_updated":"2019-06-07T14:23:14Z","snapshot_observed_at":"2026-07-06T07:07:16.383681Z","submitted_at":"2018-10-10T16:30:02Z","title":"A Closer Look at Structured Pruning for Neural Network Compression","version":3},"cited_work":{"arxiv_id":"1810.04622","doi":null,"metadata_source":"pith","pith_arxiv_id":"1810.04622","snapshot_observed_at":"2026-08-12T11:08:52.420614Z","title":"A Closer Look at Structured Pruning for Neural Network Compression","venue":"stat.ML","work_id":"8832c5a3-2d52-4cd0-a473-9ee6f679acc9","year":2018},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":1999,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:51.999123Z"},"links":{"cited_paper":"/paper/1810.04622","citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:ad0af904499cdcb7d8a4f55089ea55d3f56c747367e1f83d96625a1fdd6a3eac","observation_id":"c1c1db10-040b-4cf2-9aff-ed018dc03329","resolution":{"observed_at":"2026-08-12T11:08:52.425710Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:08:52.473399Z","title":"For a given CNN, to construct matrix G, we first extract latent features from the CNN by feed- forwarding the training data to each CNN layer","venue":null,"work_id":"1da43468-91f7-4837-92fa-450914ea4e2b","year":2022},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.075810Z"},"links":{"citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:bff853cd3b1bc66f17c12cc86fc66162049568eefb4059b29e14f960a89f3992","observation_id":"2cb4fe02-4757-48bf-9231-bef610b524db","resolution":{"observed_at":"2026-08-12T11:08:52.479300Z","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":"2026-08-12T11:08:52.504740Z","title":null,"venue":null,"work_id":"a6f438b7-b4a7-4d68-8e7a-6d89250f0696","year":2017},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.066570Z"},"links":{"citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:40ed40b57841955a8a1c93519605283345f8f3f7f0c44f5d87c007f2dba33a54","observation_id":"2420d481-3510-4cdd-9903-c02c65002d7e","resolution":{"observed_at":"2026-08-12T11:08:52.508795Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1704.04861","last_updated":"2017-04-17T03:57:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-04-17T03:57:34Z","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.04861","snapshot_observed_at":"2026-08-12T11:08:52.013412Z","title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.013412Z"},"links":{"cited_paper":"/paper/1704.04861","citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:e816107fa4727994d34ac8d06577f627ac08567d3295fcd81037ae93d31f6ea8","observation_id":"53a04c5a-338e-40a1-becd-7247c4aab8ed","resolution":{"observed_at":"2026-08-12T11:08:52.013412Z","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":"2026-08-12T11:08:52.518889Z","title":null,"venue":null,"work_id":"11fd5721-4ad9-4f38-8371-387f2afdc83b","year":2024},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.061933Z"},"links":{"citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:8ab6fb6152926e26672dbec2cc3cf9f598416ea2cffd5da0f83aa27b37d1b9a2","observation_id":"4b607579-8987-4813-9a37-ef31358915eb","resolution":{"observed_at":"2026-08-12T11:08:52.525221Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2009.08576","last_updated":"2021-03-21T21:38:32Z","snapshot_observed_at":"2026-08-12T15:13:47.746427Z","submitted_at":"2020-09-18T01:13:38Z","title":"Pruning Neural Networks at Initialization: Why are We Missing the Mark?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.08576","snapshot_observed_at":"2026-08-12T11:08:52.003694Z","title":"Pruning neural networks at initialization: Why are we missing the mark? arXiv preprint arXiv:2009.08576 ,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.003694Z"},"links":{"cited_paper":"/paper/2009.08576","citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:c940adb432b8abb814e60dc3b3f42bcedfbcba85ed5a181625a468afc343f2bc","observation_id":"336bdf9c-49cb-4070-9e8f-1a8e4a894532","resolution":{"observed_at":"2026-08-12T11:08:52.003694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1510.00149","last_updated":"2016-02-15T06:25:40Z","snapshot_observed_at":"2026-08-04T16:59:47.843960Z","submitted_at":"2015-10-01T09:03:44Z","title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1510.00149","snapshot_observed_at":"2026-08-12T11:08:52.008043Z","title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.008043Z"},"links":{"cited_paper":"/paper/1510.00149","citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:f98e1f0a4b67ae24e266337b1e88f48e7c008abd65e45bd7be2caa5a1d491884","observation_id":"fb8ffe4b-abc5-4222-928a-262e8f19e291","resolution":{"observed_at":"2026-08-12T11:08:52.008043Z","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":"2026-08-12T11:08:52.056774Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.056774Z"},"links":{"citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:28cec6f9b31892f42b613d93387633f8457b6d86be40ec721389dda5909c9732","observation_id":"bc32d006-0c9a-48f2-843d-a7844e5247cf","resolution":{"observed_at":"2026-08-12T11:08:52.056774Z","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":"2026-08-12T11:08:52.553025Z","title":"net/forum?id=sTECq7ZjtKX","venue":null,"work_id":"5f8b36c7-3b7f-4be3-a4a1-c46a441e055e","year":2013},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.032887Z"},"links":{"citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:ad81a381c5c40e5235a17db8bc8b05e5df5ad8caa4eb354db4b5b6f7a7b3d197","observation_id":"0c07e983-6a4d-488f-a133-7c987c223c4d","resolution":{"observed_at":"2026-08-12T11:08:52.557811Z","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":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-12T14:19:29.389332Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-12T11:08:52.041988Z","title":"Very deep convolutional networks for large-scale image recognition","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.041988Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:69548f4cb758d1326bfef8c1cacf5e57582bbe0053b9a08d6b11e396bcc25223","observation_id":"d70f7832-d972-47cb-8d7f-43bc65d9fab7","resolution":{"observed_at":"2026-08-12T11:08:52.041988Z","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":"2024.33764","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:08:52.273673Z","title":"Shujian Yu and Jose C Principe","venue":null,"work_id":"04aa874b-a749-47bd-a841-e3dc166cbbdb","year":2024},"citing_paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-12T11:08:52.051343Z"},"links":{"citing_paper":"/paper/2411.18578"},"observation_digest":"sha256:718595a6bd9d44ed5d24e3ec5fc396fe4ee26506c771645e36eec30968ea0759","observation_id":"84801135-db5c-4dc3-8e2e-0a94c467409a","resolution":{"observed_at":"2026-08-12T11:08:52.283223Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"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"}}],"paper":{"arxiv_id":"2411.18578","last_updated":"2024-11-27T18:23:59Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T11:02:05.602927Z","submitted_at":"2024-11-27T18:23:59Z","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":9,"verified_exact":3,"verified_fuzzy":5},"total_outbound_references":19},"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 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2411.18578."}