{"as_of":"2026-08-10T02:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f1fb752fe0b9b48af968d1104a951d63edbd4585ed08bec2569fa29c2b6ae57d","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T05:44:07.648913Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2502.08308/citation-record","integrity":"/paper/2502.08308/integrity","json":"/paper/2502.08308/citation-record.json","paper":"/paper/2502.08308"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T05:44:08.163761Z","title":"Global sparse momentum SGD for pruning very deep neural networks,","venue":null,"work_id":"3d3377f5-4aef-4a07-ba6d-76a9f1b7e658","year":2019},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.485119Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:21659d0ea3f5c189aafd75931b309d1b3b68b378f79eb1d1ba56e60a8ce124c7","observation_id":"0a6c2c69-6ad9-4664-98e9-bac7390f8e08","resolution":{"observed_at":"2026-08-08T05:44:08.167193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.11921","last_updated":"2024-02-14T16:43:50Z","snapshot_observed_at":"2026-08-09T16:21:11.995389Z","submitted_at":"2022-05-24T09:29:02Z","title":"Compression-aware Training of Neural Networks using Frank-Wolfe","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.11921","snapshot_observed_at":"2026-08-08T05:44:07.489121Z","title":"Compression-awa re training of neural networks using Frank- Wolfe,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.489121Z"},"links":{"cited_paper":"/paper/2205.11921","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:8cc1a193b380d75b1f558a5668a6ad4da2ade762c61b1e5548979a2a0cae540c","observation_id":"48ed2c98-5c22-4a78-8e03-3655ab758e03","resolution":{"observed_at":"2026-08-08T05:44:07.489121Z","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":"2026-08-08T05:44:08.153788Z","title":"Adaptive subgradient methods for online learning and stochastic optimization.,","venue":null,"work_id":"531a8749-2fd8-4e09-b047-b2f2799c68ab","year":2011},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.493128Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:c8c5c842bf2524670d84a3fb7a6bdc229cfce1517cf46dd23895e0a6a8463f19","observation_id":"49470e74-0a91-4559-840a-aba5ade95bf9","resolution":{"observed_at":"2026-08-08T05:44:08.157447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1002.4908","last_updated":"2010-07-07T19:07:16Z","snapshot_observed_at":"2026-07-06T02:04:37.023313Z","submitted_at":"2010-02-26T01:36:34Z","title":"Adaptive Bound Optimization for Online Convex Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1002.4908","snapshot_observed_at":"2026-08-08T05:44:07.496831Z","title":"Adaptive bound optimizat ion for online convex optimization,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.496831Z"},"links":{"cited_paper":"/paper/1002.4908","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:6d94090506cab7366e9162574aba463dec2e55fa58a5a65e7d17847aebd9ef9e","observation_id":"432ac540-6f2f-450c-8396-04a9614edcdb","resolution":{"observed_at":"2026-08-08T05:44:07.496831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-08T05:44:07.500843Z","title":"Adam: a method for stochastic optimizatio n,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.500843Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:f14c91bf24f5e0f3b5212084557af7a64bc61d1ff0ad065a130a38c5f5f4cd30","observation_id":"a0014507-ac26-47e3-80e5-f19f12b7ad8a","resolution":{"observed_at":"2026-08-08T05:44:07.500843Z","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":"2026-08-08T05:44:08.143939Z","title":"Lecture 6.5-rmsprop, course ra: Neural networks for machine learning,","venue":null,"work_id":"7317dbea-9866-4c10-b822-32cfbd344699","year":2012},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.504889Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:2ea6ae8b1b6f6231e2fad64005a7ed9e5b4053c9aff28bd0b06ff72a2d1c9fe7","observation_id":"bdd03782-02f8-4ae0-897a-75da1f43b71e","resolution":{"observed_at":"2026-08-08T05:44:08.147687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1212.5701","last_updated":"2012-12-22T15:46:49Z","snapshot_observed_at":"2026-07-06T03:02:42.105044Z","submitted_at":"2012-12-22T15:46:49Z","title":"ADADELTA: An Adaptive Learning Rate Method","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1212.5701","snapshot_observed_at":"2026-08-08T05:44:07.508902Z","title":"ADADELTA: an adaptive learning rate metho d,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.508902Z"},"links":{"cited_paper":"/paper/1212.5701","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:71ab2d84ce9cdfdd62784bf452185e49a53ed66677beb22d0080639218da5e6d","observation_id":"aad9f289-83a1-411e-a2f1-9a278b8531e0","resolution":{"observed_at":"2026-08-08T05:44:07.508902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.01757","last_updated":"2025-01-29T12:44:33Z","snapshot_observed_at":"2026-08-09T19:45:16.695370Z","submitted_at":"2022-03-03T15:10:40Z","title":"Complexity and performance for two classes of noise-tolerant first-order algorithms","version":3},"cited_work":{"arxiv_id":"2203.01757","doi":null,"metadata_source":"pith","pith_arxiv_id":"2203.01757","snapshot_observed_at":"2026-08-08T05:44:07.784947Z","title":"Complexity and performance for two classes of noise-tolerant first-order algorithms","venue":"math.OC","work_id":"90ebe672-a383-4b6d-b030-b5aaf677300a","year":2022},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.512737Z"},"links":{"cited_paper":"/paper/2203.01757","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:ce43a548699d05abb774c39ea7848a6c38e3bfeb3199cec48621117b9fb78db7","observation_id":"6bdd45c5-518f-468a-8611-0c59bcb7a5ba","resolution":{"observed_at":"2026-08-08T05:44:07.791134Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:08.132650Z","title":"Multilevel objective-function-free optimization with an application to neural networks training,","venue":null,"work_id":"4f481d34-3bf8-48ce-ab6d-1b2cb448a61b","year":2023},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.516264Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:2b2e0c421502c2d6491aebfa105550535df62c303e9329c7f305467c00de8505","observation_id":"34f6a788-84ae-4e88-9182-f005e8163730","resolution":{"observed_at":"2026-08-08T05:44:08.136908Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:08.121631Z","title":"Complexity of a cl ass of first-order objective-function-free opti- mization algorithms,","venue":null,"work_id":"2ceacbe7-74aa-4419-9218-8a3de10b9b80","year":2024},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.519339Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:2505e8edc17bd76ee129c8c70d75c079a7799fd94101e114e09e160081ed54f2","observation_id":"8440792b-70f5-413a-b447-9dd655f03290","resolution":{"observed_at":"2026-08-08T05:44:08.125401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:08.110292Z","title":null,"venue":null,"work_id":"1b29d893-eb84-45aa-bc4c-d70b147cb5de","year":2000},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.522541Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:b8953fff158f20d19d29107b7c4ead537fd7705c87a107e045df2392e48074d9","observation_id":"78249044-402e-4964-8ee6-46f2036ae5cf","resolution":{"observed_at":"2026-08-08T05:44:08.114146Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:08.099547Z","title":"Recent advances in trust region algorithm s,","venue":null,"work_id":"c1c75423-249f-4621-9c33-95daea1730ab","year":2015},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.526011Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:74de165dc4675ae78f35efc486bc66dcdf3d2dfc1f939a4ad902d277478ab9ab","observation_id":"ff8a1a47-819d-49cd-a8e4-97518204c849","resolution":{"observed_at":"2026-08-08T05:44:08.103570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:08.089937Z","title":"Pruning algorithms-a survey,","venue":null,"work_id":"27c5c604-55eb-4f4b-8f70-891b3dc9956b","year":1993},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.529203Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:e45527d7fe7adc036e04fe9467990b81dbae5f0006b4a3e404274703b7e42827","observation_id":"787b9975-38fb-400e-b12e-5a70843ef853","resolution":{"observed_at":"2026-08-08T05:44:08.093269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:08.080268Z","title":"Optimal brain damage ,","venue":null,"work_id":"1d3d7dd1-eb7e-415a-ae0f-652c9dcca807","year":1989},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.532370Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:4179827e3d281a6ef72eb8ee0d9f0fb5a40c97cbf92f4364135f88fc45e212bb","observation_id":"1cd4e7fb-f461-4e5b-9cf1-7b840d29e43c","resolution":{"observed_at":"2026-08-08T05:44:08.083582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.01878","last_updated":"2017-11-13T18:40:16Z","snapshot_observed_at":"2026-08-05T00:54:50.482913Z","submitted_at":"2017-10-05T04:26:49Z","title":"To prune, or not to prune: exploring the efficacy of pruning for model compression","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.01878","snapshot_observed_at":"2026-08-08T05:44:07.535710Z","title":"To prune, or not to prune: exploring the efficacy of pruning for model compres- sion,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.535710Z"},"links":{"cited_paper":"/paper/1710.01878","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:ea8dacb9ad082c8626dbd08d6739819c767883f506174a4ebdcb735877725a81","observation_id":"59539711-9cbf-4173-bdf4-7354fa744c21","resolution":{"observed_at":"2026-08-08T05:44:07.535710Z","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":"2026-08-08T05:44:08.070623Z","title":"Accelerating very de ep convolutional networks for classification and detection,","venue":null,"work_id":"da4cc80c-1c99-4302-9f05-0d94620afb4c","year":1943},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.539427Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:4626d47f6e696ba639112ed74e1dc7e7e9b2eeef7268a5cb87c9017381f2aad9","observation_id":"963adfb2-efe8-447a-930a-869eb453067e","resolution":{"observed_at":"2026-08-08T05:44:08.074191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:08.061593Z","title":"Exploiting linear structure within con- volutional networks for efficient evaluation,","venue":null,"work_id":"939625eb-0cc7-47ec-b8b5-a1f223b9df5c","year":2014},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.542622Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:158a86ab3c78c3fd7e80f841fa9c07b4d0e112eb1b744b535e86c70ea911a663","observation_id":"234e9ae3-c3ed-4920-ab13-3a9b1a135d76","resolution":{"observed_at":"2026-08-08T05:44:08.064900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:08.051821Z","title":"On compressing deep mo dels by low rank and sparse decomposi- tion,","venue":null,"work_id":"fa838617-4631-4d83-a734-e99ac845e803","year":2017},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.546136Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:03c346f7ca2676d2aea179f2bc4ad981a3975415990c17b6e3bf5ce0c1588824","observation_id":"a5bbed32-994f-4c13-8f2f-83ee80e74778","resolution":{"observed_at":"2026-08-08T05:44:08.055279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1602.02830","last_updated":"2016-03-17T14:54:25Z","snapshot_observed_at":"2026-07-06T04:45:31.463087Z","submitted_at":"2016-02-09T01:01:59Z","title":"Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.02830","snapshot_observed_at":"2026-08-08T05:44:07.549128Z","title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or -1,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.549128Z"},"links":{"cited_paper":"/paper/1602.02830","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:c36a6c0011f0b53a4d748aab68e492705f5b8ce30413c671716f8a8814ec1bb4","observation_id":"782d0a6d-1ed7-4899-a272-4b7cb3bc47c0","resolution":{"observed_at":"2026-08-08T05:44:07.549128Z","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":"2026-08-08T05:44:08.041596Z","title":"T wo-step quantization for low-bit neural networks,","venue":null,"work_id":"04e2509b-c265-47f4-ae66-5968fd32b0c7","year":2018},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.552875Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:4414adef62ed50e6af832bc4d6cf337b9c487a64f24fb3261f7f786d2f69d332","observation_id":"c73fc56c-5894-484b-b002-0099341f2f0b","resolution":{"observed_at":"2026-08-08T05:44:08.045008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:08.032300Z","title":"Position-based scaled grad ient for model quantization and pruning,","venue":null,"work_id":"6f10b139-c055-4fa0-9be4-2020fbba843b","year":2020},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.555899Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:97cec044db1c836258db6cda9bf6787de8c6e220715e52c657370a870e0b7b4e","observation_id":"1de44bc3-29a1-4a72-850d-c57998208ecd","resolution":{"observed_at":"2026-08-08T05:44:08.035736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:08.022536Z","title":"Learning both wei ghts and connections for efficient neural network,","venue":null,"work_id":"e279be92-e99a-4e53-9a28-ac33ae37461c","year":2015},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.558945Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:81146022b883feae7dbbf9b093ad5b713b715235405211c679b1def1c8e495fc","observation_id":"82dbd911-f05c-4667-aace-d4313a2b208f","resolution":{"observed_at":"2026-08-08T05:44:08.026480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:08.011783Z","title":"Exploiting sparsene ss in deep neural networks for large vocabulary speech recognition,","venue":null,"work_id":"c20056c8-9d84-4dd3-8686-4aa9dc2ed2da","year":2012},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.561980Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:0ba201e9f4ef8ea75f631ca33c98a5ccf015d43d2c8863cec9b199d2754e0251","observation_id":"a8bf48b1-27cb-42b9-8ea1-d22ea80a8864","resolution":{"observed_at":"2026-08-08T05:44:08.015428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.01312","last_updated":"2018-06-22T14:54:59Z","snapshot_observed_at":"2026-07-06T06:12:42.842258Z","submitted_at":"2017-12-04T19:20:27Z","title":"Learning Sparse Neural Networks through $L_0$ Regularization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.01312","snapshot_observed_at":"2026-08-08T05:44:07.565078Z","title":"Learning spar se neural networks through ℓ0 regularization,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.565078Z"},"links":{"cited_paper":"/paper/1712.01312","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:d4a93e9ecc6c6e19483f42727756fb2d17d412320d5ae856f549617ddd1a89f9","observation_id":"d1923a57-1c27-41a9-bda4-fbd8e3cc8c76","resolution":{"observed_at":"2026-08-08T05:44:07.565078Z","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":"2026-08-08T05:44:08.002090Z","title":"Compression-aware trai ning of deep networks,","venue":null,"work_id":"52f5e5db-3998-4ebc-8dde-b5b7ddbf259f","year":2017},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.568405Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:d5f341c5fdb8932a6dcc3acc1d6e9ce1d2872a10ac32b2a41e97029715d2f952","observation_id":"8314f3a6-b149-4c20-aa07-ac7dc0601976","resolution":{"observed_at":"2026-08-08T05:44:08.005762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1607.03250","last_updated":"2016-07-12T07:43:01Z","snapshot_observed_at":"2026-07-06T05:03:13.994105Z","submitted_at":"2016-07-12T07:43:01Z","title":"Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.03250","snapshot_observed_at":"2026-08-08T05:44:07.571649Z","title":"Network trimming: A data-driven neuron pruning approach towards efficient deep architectures,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.571649Z"},"links":{"cited_paper":"/paper/1607.03250","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:e0c89e66cc1f22c366e0b51854d927c96dce0d94be7ead0867ede6bda724e576","observation_id":"8314ac11-3659-4278-b4af-35807abfae8d","resolution":{"observed_at":"2026-08-08T05:44:07.571649Z","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":"2026-08-08T05:44:07.991280Z","title":"Sparsity in deep learning: Pruning and growth for efficient inference and training in neural network s,","venue":null,"work_id":"c886a5ea-4191-4992-a2c7-d34696b39663","year":2021},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.574975Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:030bffc99374640035a28c2cdd2bf2c4d2033561d55fd67ece506bc6e040fd92","observation_id":"e0ca11c7-bb4b-49fa-895c-d7070a365681","resolution":{"observed_at":"2026-08-08T05:44:07.995490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:07.980937Z","title":"Dynamic network surgery for efficient DNNs,","venue":null,"work_id":"18d8e2f8-2e30-42a8-a6ca-23a12760713d","year":2016},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.578578Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:d2668b660a44bafd2c93c7cd1b52adba19127d02800ccd42c560e79ba97bf4d1","observation_id":"790b78b8-486c-4b01-a573-446efe91552c","resolution":{"observed_at":"2026-08-08T05:44:07.984412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:07.970806Z","title":"Scalable training of artificial neural networks with adaptive sparse connectivi ty inspired by network science,","venue":null,"work_id":"ce4e1f9c-bca8-44ef-86af-2a3e9a3bc7c7","year":2018},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.582238Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:e86a924151e1f63d0465ba3d333fefe4ef03d653a21b52a500d588434d12f4c1","observation_id":"1c0e3fd9-9018-49da-a554-3d3ea0db14c1","resolution":{"observed_at":"2026-08-08T05:44:07.974413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1808.06866","last_updated":"2018-08-21T12:22:38Z","snapshot_observed_at":"2026-07-06T06:56:39.566826Z","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":"2026-08-08T05:44:07.585567Z","title":"Soft filter pru ning for accelerating deep convolutional neural networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.585567Z"},"links":{"cited_paper":"/paper/1808.06866","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:3d3aa9ff1825e4f857bb0fb5de734223e9655885d661e50ff9518b977a50c5fe","observation_id":"7935c2bd-7dba-4c92-9498-d0236e17db73","resolution":{"observed_at":"2026-08-08T05:44:07.585567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.07243","last_updated":"2020-10-21T15:46:05Z","snapshot_observed_at":"2026-07-06T10:04:30.196342Z","submitted_at":"2020-10-14T17:03:58Z","title":"Deep Neural Network Training with Frank-Wolfe","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.07243","snapshot_observed_at":"2026-08-08T05:44:07.589788Z","title":"Deep neural netw ork training with Frank-Wolfe,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.589788Z"},"links":{"cited_paper":"/paper/2010.07243","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:b5a2b52cdf204b54dfc258b1c2ed56d89dee6e73a19c5cf6a9c8570081affc0c","observation_id":"b50b5fd4-0f93-4cb2-a287-f79d13afdc67","resolution":{"observed_at":"2026-08-08T05:44:07.589788Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-08T05:44:07.593473Z","title":"Pruning convolutional neural networks for resource efficient inference,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.593473Z"},"links":{"cited_paper":"/paper/1611.06440","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:b96881bac9731eaa074d2e7d273fd1aa2c7188c45b66b888c14ab35db1881aa7","observation_id":"f228a0b5-b228-4277-8c02-7ad9b0b53b5d","resolution":{"observed_at":"2026-08-08T05:44:07.593473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.05787","last_updated":"2018-07-09T10:38:35Z","snapshot_observed_at":"2026-08-07T19:42:19.497303Z","submitted_at":"2018-01-17T18:34:33Z","title":"Faster gaze prediction with dense networks and Fisher pruning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.05787","snapshot_observed_at":"2026-08-08T05:44:07.596766Z","title":"Fas ter gaze prediction with dense networks and Fisher pruning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.596766Z"},"links":{"cited_paper":"/paper/1801.05787","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:e3c9547e5bb1df18f1f74b8d3da3f5f686164aac880849967d45b8167e17002f","observation_id":"e2b60522-f285-4510-8c79-ede53eed9567","resolution":{"observed_at":"2026-08-08T05:44:07.596766Z","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":"2026-08-08T05:44:07.959569Z","title":"Learn ing pruning-friendly networks via Frank-Wolfe: One-shot, any-sparsity, and no retraining,","venue":null,"work_id":"75a7780d-076e-4213-8633-ed946b15eca0","year":2022},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.600962Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:85e9e8485ccabf8f547fd4ad698cdf5bac3c2cd323f74c76b0ecf68f31628ef9","observation_id":"f43b615d-559d-45cf-8949-68032ddf7580","resolution":{"observed_at":"2026-08-08T05:44:07.963568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:07.949465Z","title":"Sparse predicti on with the k-support norm,","venue":null,"work_id":"1fa6a26c-eff2-4b34-b627-2a06ae618a48","year":2012},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.604840Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:2ed1022d310868415761531c22be03d701d2b86a2e143ebca5b360925a97500c","observation_id":"49337144-6908-45ff-a7b1-509455272d5a","resolution":{"observed_at":"2026-08-08T05:44:07.953074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:07.939216Z","title":"The group k-support norm for learning with structured sparsity,","venue":null,"work_id":"6274078d-e226-4e96-bbe7-6cc864ca27a5","year":2017},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.608245Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:706d2de5ccaabfb1813d8019cfbc0d83f4294dc613e50cf41cab3d22c4740399","observation_id":"236a8c90-a82d-4583-b51e-0427c83675b7","resolution":{"observed_at":"2026-08-08T05:44:07.942849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:07.929051Z","title":"An algorithm for quadratic programming,","venue":null,"work_id":"a2c14476-26fc-419d-ad06-5a050e567850","year":1956},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.611645Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:03eceb80650ef82f03a81c65daa00b859c28da0f9f9a9c7cbbc12ce6fa824271","observation_id":"a7aa2ce3-dbbc-4efd-b470-ae959bbd8bbe","resolution":{"observed_at":"2026-08-08T05:44:07.932402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:07.919540Z","title":"Constrained minimizati on methods,","venue":null,"work_id":"de944039-743a-4bd8-a089-7bf81a48522d","year":1966},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.614825Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:9f4cfe2cca3265fff16ea15f3f96403301c8a08ee56a60caebefc5452b4ca737","observation_id":"61233321-6eef-4ffd-8d80-8059dbec785a","resolution":{"observed_at":"2026-08-08T05:44:07.922943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:07.909210Z","title":"Stochasti c Frank-Wolfe methods for nonconvex optimiza- tion,","venue":null,"work_id":"df8e63ed-c7a1-43f5-8916-773fb68eb7b5","year":2016},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.617929Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:908de02163e8a214a4bb6ae68974f71f5527332e534fd538404e2de051534bfe","observation_id":"4f84543b-97f8-4375-82a6-db4a20e53f20","resolution":{"observed_at":"2026-08-08T05:44:07.912981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.02865","last_updated":"2020-11-19T20:31:14Z","snapshot_observed_at":"2026-07-06T06:27:10.408557Z","submitted_at":"2018-03-07T20:30:35Z","title":"WNGrad: Learn the Learning Rate in Gradient Descent","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.02865","snapshot_observed_at":"2026-08-08T05:44:07.620866Z","title":"Wngrad: Learn the learnin g rate in gradient descent,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.620866Z"},"links":{"cited_paper":"/paper/1803.02865","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:0922f9a872b1cc0fb332349d2908a7e9402824d95ac928d9dc1d3fc3315a5be9","observation_id":"fce8ebf8-de76-4af6-adb5-88c91bea043c","resolution":{"observed_at":"2026-08-08T05:44:07.620866Z","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":"2026-08-08T05:44:07.899460Z","title":"On the Lambert W function,","venue":null,"work_id":"58906d69-6ffb-4fd3-88a1-813fce590a65","year":1996},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.624279Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:a6ae0caae0b0d15be209135f9a4529a7e50427706f2068be720df72e4f5e6586","observation_id":"220700b2-20c2-443b-a2fe-ff756f45dbc1","resolution":{"observed_at":"2026-08-08T05:44:07.902915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:07.888847Z","title":"Sparco: A testing framework for sparse reconstruction,","venue":null,"work_id":"9a028712-4e33-49d2-8a2e-1d2b5c9be1ab","year":2007},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.627293Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:486e40a3e4f0ac5ed6d132b02914722570f2e9728d656331c4b622a8f4ca3c95","observation_id":"17021cd3-7042-4fc6-85fa-60fdf00c4d7c","resolution":{"observed_at":"2026-08-08T05:44:07.892258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.07812","last_updated":"2024-07-10T16:32:21Z","snapshot_observed_at":"2026-07-06T18:44:20.262496Z","submitted_at":"2024-07-10T16:32:21Z","title":"S2MPJ and CUTEst optimization problems for Matlab, Python and Julia","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.07812","snapshot_observed_at":"2026-08-08T05:44:07.630411Z","title":"S2MPJ and CUTEst optimization problems for Matlab, Python and Julia","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.630411Z"},"links":{"cited_paper":"/paper/2407.07812","citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:2dff334342498b542058d24ff84a5890c94649d14ee53dc109a374d1ce9e9cf4","observation_id":"f2ab038a-bff9-4808-a6be-432b4efa0f9d","resolution":{"observed_at":"2026-08-08T05:44:07.630411Z","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":"2026-08-08T05:44:07.878205Z","title":"A fast algorit hm for sparse reconstruction based on shrinkage, subspace optimization, and continuation,","venue":null,"work_id":"9872021a-00f9-4479-94f5-5e979f61843d","year":2010},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.634526Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:bcdb60bba83732f2a512f9867d3d90e42db6e68f55815c082b5e00a12f73612e","observation_id":"a27cb739-6d3c-4083-8f94-72daf21a933c","resolution":{"observed_at":"2026-08-08T05:44:07.881806Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:07.868898Z","title":"A variable fixing version of the two-block nonlinear constrained Gauss-Seidel algorithm for ℓ 1-regularized least-squares,","venue":null,"work_id":"c8206e00-aa77-453a-9846-94fbf7ede99f","year":2014},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.637718Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:fc1f4eb48336d4dc068059bae6ce4b018b40f4046cdd6c4934887eb600793766","observation_id":"65bca56e-8161-4e2e-88aa-ae62ea6c3413","resolution":{"observed_at":"2026-08-08T05:44:07.872375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:07.859721Z","title":"K-SVD: An algori thm for designing overcomplete dictionaries for sparse representation,","venue":null,"work_id":"70d1d689-15e0-479a-84be-311137519b11","year":2006},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.641002Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:377481a01f7243ed71ae3b00e6e823733bfdc276c26853ef451aa15269f58715","observation_id":"1513203e-f6be-43cb-a5e7-10cd04d69ea2","resolution":{"observed_at":"2026-08-08T05:44:07.863187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:07.849223Z","title":"UCI machine learning repository,","venue":null,"work_id":"6e827475-78e5-40b5-858b-2a1ed3e8a0b3","year":2013},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.645377Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:4234c4e0dd36ce4169727f88bc9e222f34e2007782bc09429ce2c99bbcd22a83","observation_id":"4217440c-ea27-44d6-9ee5-194b43ee4ff2","resolution":{"observed_at":"2026-08-08T05:44:07.852794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T05:44:07.839013Z","title":"LIBSVM: A library for suppor t vector machines,","venue":null,"work_id":"5a66597e-5e1a-43ee-980f-0e4d6f67a19f","year":2011},"citing_paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-08T05:44:07.648913Z"},"links":{"citing_paper":"/paper/2502.08308"},"observation_digest":"sha256:62e55734a2e0ce2451d6efe23b29aa67979426155f78515ab7161efc2c30d23e","observation_id":"ce725356-6471-41a6-be3b-d69314cbb7cc","resolution":{"observed_at":"2026-08-08T05:44:07.842732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.08308","last_updated":"2025-02-12T11:22:23Z","latest_version":1,"primary_category":"math.OC","snapshot_observed_at":"2026-08-09T19:44:45.202487Z","submitted_at":"2025-02-12T11:22:23Z","title":"prunAdag: an adaptive pruning-aware gradient method"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":32},"total_outbound_references":48},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2502.08308."}