{"as_of":"2026-08-21T15:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:577ecafec169a73230ddbfc3a89a680363a488e0e10060b0698e8642f213d21b","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T12:41:04.481868Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T23:08:02.945208Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-29T23:14:01.885058Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"cited_work":{"arxiv_id":"1908.07380","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.07380","snapshot_observed_at":"2026-06-29T23:14:01.885058Z","title":"M., and Szepesv \\' a ri, C","venue":null,"work_id":"1310e298-8741-4fd9-973f-9231b0d3842d","year":1908},"citing_paper":{"arxiv_id":"2310.11203","last_updated":"2026-05-19T09:15:59Z","snapshot_observed_at":"2026-08-16T19:21:26.633990Z","submitted_at":"2023-10-17T12:29:29Z","title":"Federated Learning with Nonvacuous Generalisation Bounds","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-24T06:09:45.249831Z"},"links":{"cited_paper":"/paper/1908.07380","citing_paper":"/paper/2310.11203"},"observation_digest":"sha256:794e312193876475fdc4fd624f2d0186db879678aea85dfc9cd55e6a786dc650","observation_id":"9ade8bd2-ca2a-4986-8d46-e7f0781b457d","resolution":{"observed_at":"2026-05-24T06:14:00.164084Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"cited_work":{"arxiv_id":"1908.07380","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.07380","snapshot_observed_at":"2026-06-29T23:14:01.885058Z","title":"M., and Szepesv \\' a ri, C","venue":null,"work_id":"1310e298-8741-4fd9-973f-9231b0d3842d","year":1908},"citing_paper":{"arxiv_id":"2605.15416","last_updated":"2026-08-02T00:54:42Z","snapshot_observed_at":"2026-08-16T01:55:44.908168Z","submitted_at":"2026-05-14T21:01:05Z","title":"Margin-Adaptive Confidence Ranking for Reliable LLM Judgement","version":1},"reference_index":243,"source":"arxiv_source","source_observed_at":"2026-05-19T16:05:41.505091Z"},"links":{"cited_paper":"/paper/1908.07380","citing_paper":"/paper/2605.15416"},"observation_digest":"sha256:be1d6c30c0bc9acfc5d49d3d04268b6af6f48d28e7d60b2a167da6ce46e4795f","observation_id":"65016487-7963-48ac-8667-5115eea4810c","resolution":{"observed_at":"2026-05-19T16:12:39.488832Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"cited_work":{"arxiv_id":"1908.07380","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.07380","snapshot_observed_at":"2026-06-29T23:14:01.885058Z","title":"M., and Szepesv \\' a ri, C","venue":null,"work_id":"1310e298-8741-4fd9-973f-9231b0d3842d","year":1908},"citing_paper":{"arxiv_id":"2605.26222","last_updated":"2026-05-25T18:00:05Z","snapshot_observed_at":"2026-08-14T20:08:24.693792Z","submitted_at":"2026-05-25T18:00:05Z","title":"From Privacy to Generalization: Linear Max-Information Bounds for DP-SGD","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T23:08:02.945208Z"},"links":{"cited_paper":"/paper/1908.07380","citing_paper":"/paper/2605.26222"},"observation_digest":"sha256:d2897721e84b242d0d6a373e892fa5449ca0af9b5477d6866ad076fadc0f0b4a","observation_id":"65a59c03-e14f-4e36-bfd2-0023902289e0","resolution":{"observed_at":"2026-06-29T23:14:01.886602Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1908.07380/citation-record","integrity":"/paper/1908.07380/integrity","json":"/paper/1908.07380/citation-record.json","paper":"/paper/1908.07380"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:41:04.935270Z","title":"Weight uncertainty in neural networks","venue":null,"work_id":"de801cb8-9283-4ae0-bc85-bbebaf977b63","year":2015},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.337904Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:b425bd1aa65c155a0f9c0bdc6d2e5c2d40a738365ec621177b6caef91722ae66","observation_id":"c3ee75ee-7f84-4c12-a5ba-3fb50034cf06","resolution":{"observed_at":"2026-08-14T12:41:04.940232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.919153Z","title":"Stochastic gradient descent tricks","venue":null,"work_id":"dc51f4f4-9008-4454-bb53-f701994f2284","year":2012},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.343984Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:5423afa86a2966521011cc9cb284c9c1875c7a88a2fd60074fd91ca7382a6e01","observation_id":"6a0bbc1a-43e7-4638-bdf2-e15579fe67ab","resolution":{"observed_at":"2026-08-14T12:41:04.924666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:41:04.349270Z","title":"Concentration inequalities: A nonasymptotic theory of independence","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.349270Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:f483c41a94413b242e5f6ae5c1725ef576a6f189545dcab2b2458890dd9f5a46","observation_id":"076c540c-f705-4684-a7bb-92fe3d42e77b","resolution":{"observed_at":"2026-08-14T12:41:04.349270Z","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":"2026-08-14T12:41:04.893726Z","title":"Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping","venue":null,"work_id":"9fb9c996-ee1e-4f3c-bb63-c70fbcf5074b","year":2001},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.354673Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:bfe6b4c32469cf45f26d181de527995d1747ae3ebf2a467f5f1a47b6605b504f","observation_id":"b5a71cef-32db-4ac1-8aa9-03f950df3bee","resolution":{"observed_at":"2026-08-14T12:41:04.898727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.878898Z","title":"UCI Machine Learning Repository , 2017","venue":null,"work_id":"565b92cc-38e7-411f-9b04-556256423165","year":2017},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.360435Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:f9a040c20a5418d0ca2f846a433b51b226d9af62394817716239985f05cd8af7","observation_id":"acc0d49f-e52e-40ed-b7d2-9d2b498b1fc1","resolution":{"observed_at":"2026-08-14T12:41:04.883935Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.864628Z","title":null,"venue":null,"work_id":"5dff855b-4d55-48d5-8806-99846e3e5ac3","year":2017},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.365600Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:b98e764a71df6c99016dc1a8ddaa5574ca6be2ed243982eec2f4219ae24c3048","observation_id":"d2db745e-7c92-447b-9949-afa6008203ed","resolution":{"observed_at":"2026-08-14T12:41:04.869121Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.850655Z","title":"Data-dependent PAC-Bayes priors via differential privacy","venue":null,"work_id":"4e5041cf-ec79-496f-9839-919ce82c3299","year":2018},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.371165Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:181ed59eb577712e1177a67a48e5f12f07030ce06d6c785e44a176cc32557d67","observation_id":"16ccacaa-ed78-4e80-853d-299eedbf6e2e","resolution":{"observed_at":"2026-08-14T12:41:04.855510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.835690Z","title":"Self bounding learning algorithms","venue":null,"work_id":"ed09b75b-db23-4e18-964f-c36ed6e4d5ac","year":1998},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.376130Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:3183cd5d30253aaa496163aa2831f5a25e005fae715666175e309f374269bd50","observation_id":"4d9db373-9e1f-4ecf-9e27-5dfa09ce6e83","resolution":{"observed_at":"2026-08-14T12:41:04.840445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.820522Z","title":"Keeping neural networks simple","venue":null,"work_id":"457a2f7d-9280-4912-9ebb-d718d66e7005","year":1993},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.381119Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:3c31753eed3d49e9d341d8ade8e1f13c7e44ee3cabb9cbf26a6472e906e87744","observation_id":"6741e1a2-9238-405d-8e9c-beb76c52d5a3","resolution":{"observed_at":"2026-08-14T12:41:04.825398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.01851","last_updated":"2018-07-05T16:28:24Z","snapshot_observed_at":"2026-08-21T02:59:56.931032Z","submitted_at":"2018-06-05T18:00:01Z","title":"Pathwise Derivatives Beyond the Reparameterization Trick","version":2},"cited_work":{"arxiv_id":"1806.01851","doi":null,"metadata_source":"pith","pith_arxiv_id":"1806.01851","snapshot_observed_at":"2026-08-14T12:41:04.572871Z","title":"Pathwise Derivatives Beyond the Reparameterization Trick","venue":"stat.ML","work_id":"3a02e9f2-eae5-4e8e-8941-633a6138f6a2","year":2018},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.385856Z"},"links":{"cited_paper":"/paper/1806.01851","citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:37e3d25b797c2eba6b4bff06f88fb244814abde5238b6399c9449d2ecb9a15e3","observation_id":"711af00f-0877-414d-bd01-b75f03a2c166","resolution":{"observed_at":"2026-08-14T12:41:04.580105Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.805926Z","title":"Microchoice bounds and self bounding learning algorithms","venue":null,"work_id":"bfb33cd7-4e2c-40a8-b64e-77bb618a57be","year":2003},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.391258Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:5a761dba1c90eefab81d16e7090a2693582f5b229a475154e1e7a0ab73328285","observation_id":"a82617be-28f1-41e7-b9c7-fb90b43227ba","resolution":{"observed_at":"2026-08-14T12:41:04.810863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.791255Z","title":"(Not) bounding the true error","venue":null,"work_id":"0a02b70d-2342-472d-b701-fd3366e8c843","year":2001},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.396304Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:92cf43875c2d0c2c7dac69402792fa45bda29477715a1e0c70a58cf5c8d84b01","observation_id":"ca9f668e-3a49-426e-87f4-3504e55fa749","resolution":{"observed_at":"2026-08-14T12:41:04.795966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.776980Z","title":"Bounds for averaging classifiers","venue":null,"work_id":"e54a6e5b-7ce0-41b6-8aac-99cf6a67f51c","year":2001},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.402647Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:ec64930d68d0ba06e1b24ac1edfa6b25311c534353daeaeb4bcc7c7e363961ce","observation_id":"2c9c05be-08d7-40d3-ab6b-e9ddc0b435ca","resolution":{"observed_at":"2026-08-14T12:41:04.781764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.761978Z","title":"Distribution-dependent PAC-Bayes priors","venue":null,"work_id":"4397abac-60a9-444e-9762-c6c1d88fd763","year":2010},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.407326Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:6544e1d65d9d8fe7d4de85a6e5191e2206c1f9b7a877d3327d91971188eed839","observation_id":"e706431f-23da-4bd7-aae1-9e4a28b7ab09","resolution":{"observed_at":"2026-08-14T12:41:04.767002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.747382Z","title":"Tighter PAC-Bayes bounds through distribution-dependent priors","venue":null,"work_id":"3189d918-38f8-463f-8d6d-2669b8e3b626","year":2013},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.412192Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:6a2c16ea016fd8387a06ca2e086e02410aeeabf0053f4595645505537a978ad1","observation_id":"27e5400e-ee69-408a-b89c-3765c524ffdb","resolution":{"observed_at":"2026-08-14T12:41:04.752310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.732820Z","title":"A PAC-Bayesian analysis of randomized learning with application to stochastic gradient descent","venue":null,"work_id":"c7441c94-394d-40d1-8d3d-a08e3bf79926","year":2017},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.417250Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:8eee2a66cb911d1f25e79bf73570d860e44dd3dc4ecba3559f64ceb5e9f7b726","observation_id":"c3251720-b698-4b4e-99ac-1113212938ab","resolution":{"observed_at":"2026-08-14T12:41:04.737706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"cs/0411099","last_updated":"2004-11-30T08:36:59Z","snapshot_observed_at":"2026-07-07T03:06:10.700436Z","submitted_at":"2004-11-30T08:36:59Z","title":"A Note on the PAC Bayesian Theorem","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"cs/0411099","snapshot_observed_at":"2026-08-14T12:41:04.422363Z","title":"A note on the PAC Bayesian theorem","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.422363Z"},"links":{"cited_paper":"/paper/cs/0411099","citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:7ecee1e55cc410729445fb7dc7ea288680b5220a067248a1c29e57ed1fbb2938","observation_id":"49429d1d-84f4-4edf-9864-d43bc58c7338","resolution":{"observed_at":"2026-08-14T12:41:04.422363Z","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":"2026-08-14T12:41:04.717284Z","title":"Bayesian Learning via Stochastic Dynamics","venue":null,"work_id":"41d03210-d856-44e1-8bd6-aa00951a012e","year":1993},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.427835Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:862c5c00fa2e50f8a57bdf34276f0c315309bf7f6f9e26ff6756a111ba6e0744","observation_id":"7a5355af-eb1c-464b-a79c-6d3e0664a39a","resolution":{"observed_at":"2026-08-14T12:41:04.722539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.701973Z","title":"Exploring generalization in deep learning","venue":null,"work_id":"93e4c738-b09b-4d7e-8879-d8af6b8f67be","year":2017},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.432861Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:5b985306d04a28df5fb4bc12f4067ec037a1a217a9bd804c1590fec27f0815e8","observation_id":"a7db3f68-cce9-4161-8b61-4199d245f90f","resolution":{"observed_at":"2026-08-14T12:41:04.706977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.09564","last_updated":"2018-02-23T22:30:45Z","snapshot_observed_at":"2026-08-14T20:44:26.541416Z","submitted_at":"2017-07-29T22:36:35Z","title":"A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.09564","snapshot_observed_at":"2026-08-14T12:41:04.437643Z","title":"A PAC-Bayesian approach to spectrally-normalized margin bounds for neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.437643Z"},"links":{"cited_paper":"/paper/1707.09564","citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:b7000a31496c61938b592c4db9759900154ba9809c105d3bd4c68e349963bc5f","observation_id":"0d1b910e-c1a7-41d6-a51f-3fea2d1e8adf","resolution":{"observed_at":"2026-08-14T12:41:04.437643Z","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":"2026-08-14T12:41:04.687058Z","title":"Robust forward algorithms via PAC-Bayes and Laplace distributions","venue":null,"work_id":"9701ec79-bb24-4e76-b29a-acf4ce14fff6","year":2014},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.442976Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:a541500838a414f0f43df44154de8429e0e4e1d6e60f6809a744917f7d2ebf33","observation_id":"0e087433-7efc-45fa-87fc-e404f8424db0","resolution":{"observed_at":"2026-08-14T12:41:04.692129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.00171","last_updated":"2018-04-20T09:27:47Z","snapshot_observed_at":"2026-08-14T19:59:03.136338Z","submitted_at":"2017-12-30T18:11:59Z","title":"PAC-Bayesian Margin Bounds for Convolutional Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.00171","snapshot_observed_at":"2026-08-14T12:41:04.447990Z","title":"PAC-Bayesian Margin Bounds for Convolutional Neural Networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.447990Z"},"links":{"cited_paper":"/paper/1801.00171","citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:4e6fe146f5aed90821a25416a333a8820931ff02a59e302f13a1f1d21c4705a3","observation_id":"c257be4b-8a51-4bfc-97fe-a0db79553257","resolution":{"observed_at":"2026-08-14T12:41:04.447990Z","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":"2026-08-14T12:41:04.672528Z","title":"A useful theorem for nonlinear devices having Gaussian inputs","venue":null,"work_id":"2813c832-3593-44e0-ab80-5ac9007ff2c6","year":1958},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.453437Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:4bfa197e1aec851c82338b6d1c4017ce29356f616c0bcddcf65c409b4cfbdb7b","observation_id":"a41b479e-b492-409c-908d-9a5c1607ca1b","resolution":{"observed_at":"2026-08-14T12:41:04.677380Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.655397Z","title":"PAC-Bayesian Generalization Error Bounds for Gaussian Process Classification","venue":null,"work_id":"28ddfe28-8438-42d5-b514-ae273244aa55","year":2002},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.458312Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:04543b28be7852fb38553fac7482a90933226ea68c7b91121364ab5673515b59","observation_id":"08c9065f-1c86-47eb-bece-ba6db019d6d9","resolution":{"observed_at":"2026-08-14T12:41:04.661288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.639428Z","title":"Understanding Machine Learning","venue":null,"work_id":"519739cb-a75e-4663-8268-7b4634af2485","year":2014},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.462840Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:62e1423ac66cc08f27a5ced642005e40d1c8f2fc66dd823917362649f536b6e7","observation_id":"2f31cfeb-8f74-4a14-90a4-a60a02cd0756","resolution":{"observed_at":"2026-08-14T12:41:04.644671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.623719Z","title":"Dropout: a simple way to prevent neural networks from overfitting","venue":null,"work_id":"aeb3f045-4aca-44b4-83a3-550fc184ceca","year":1929},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.467365Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:b8289e91c2bfe3784ee5db538e5b10f07fadd36b09389f116f24038dcfe53259","observation_id":"4034e9d5-a950-4bb8-9d86-c2733d4e6b83","resolution":{"observed_at":"2026-08-14T12:41:04.628652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.607780Z","title":"A strongly quasiconvex PAC-Bayesian bound","venue":null,"work_id":"a22cde56-317d-41d5-bab2-8a6fd2613bbd","year":2017},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.472005Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:b8261dce77d6eeda0a32b4f425d50836cf55661858d19f8415bba398a79c3325","observation_id":"efc748ac-8a6f-43d3-8382-72bcc4023ef1","resolution":{"observed_at":"2026-08-14T12:41:04.613045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T12:41:04.591366Z","title":"Regularization of neural networks using dropconnect","venue":null,"work_id":"96add75d-d579-4386-88b3-6943c7a2bd6a","year":2013},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.476896Z"},"links":{"citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:df2dac666695f856131d0183719daf1d71e6427856406bd2421363d5abfa0d0a","observation_id":"877f50e5-c8a6-4329-8470-395ae2975da1","resolution":{"observed_at":"2026-08-14T12:41:04.596721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.01996","last_updated":"2022-03-08T21:16:04Z","snapshot_observed_at":"2026-08-15T10:37:49.016375Z","submitted_at":"2019-02-06T01:29:01Z","title":"Are All Layers Created Equal?","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.01996","snapshot_observed_at":"2026-08-14T12:41:04.481868Z","title":"Are All Layers Created Equal? arXiv:1902.01996, 2019","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop","version":5},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-14T12:41:04.481868Z"},"links":{"cited_paper":"/paper/1902.01996","citing_paper":"/paper/1908.07380"},"observation_digest":"sha256:8306e794d587661b053abacaef5c37568d134c96c2d8b2c11131b03e8249bf3d","observation_id":"de8944ab-8190-47da-9370-6a782e34aeba","resolution":{"observed_at":"2026-08-14T12:41:04.481868Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1908.07380","last_updated":"2019-10-04T17:23:16Z","latest_version":5,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-21T02:58:56.918324Z","submitted_at":"2019-08-19T13:27:08Z","title":"PAC-Bayes with Backprop"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":1,"verified_fuzzy":22},"total_outbound_references":29},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 3 inbound Pith citation observations for arXiv:1908.07380."}