{"as_of":"2026-08-10T14:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1287d104d8e71e5494bf28bd9c1b374d010acef549bb94f5f16628176264efb9","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-11T02:24:42.639358Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-30T11:53:54.585915Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.07844","snapshot_observed_at":"2026-07-30T11:53:54.585915Z","title":"Decelle, A","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.27077","last_updated":"2026-07-29T16:00:59Z","snapshot_observed_at":"2026-08-08T09:55:45.334880Z","submitted_at":"2026-07-29T16:00:59Z","title":"Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-30T11:53:54.585915Z"},"links":{"cited_paper":"/paper/2605.07844","citing_paper":"/paper/2607.27077"},"observation_digest":"sha256:7f809c537db224c97c21f0a7fa5bf3eb09501deeaf3e66340e91f5a86590853d","observation_id":"2ffa2388-abad-4f65-9fed-e088d18c2db0","resolution":{"observed_at":"2026-07-30T11:53:54.585915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2605.07844/citation-record","integrity":"/paper/2605.07844/integrity","json":"/paper/2605.07844/citation-record.json","paper":"/paper/2605.07844"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1805.08522","last_updated":"2019-04-21T10:16:54Z","snapshot_observed_at":"2026-08-10T08:35:15.296599Z","submitted_at":"2018-05-22T11:51:36Z","title":"Deep learning generalizes because the parameter-function map is biased towards simple functions","version":5},"cited_work":{"arxiv_id":"1805.08522","doi":"10.48550/arxiv.1805.08522","metadata_source":"pith","pith_arxiv_id":"1805.08522","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deep learning generalizes because the parameter-function map is biased towards simple functions","venue":"stat.ML","work_id":"fac56252-c56f-47f5-87d9-16d73bdccad9","year":2018},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"cited_paper":"/paper/1805.08522","citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:9841650fd8072596325a300af3f7d57665785dc4db5b48026773c152a8b5053f","observation_id":"5c3cead8-45a8-4034-8bde-47856ab7d12c","resolution":{"observed_at":"2026-05-11T02:25:53.740589Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Sgd on neural networks learns functions of increasing complexity.Advances in neural information processing systems, 32","venue":null,"work_id":"4068f8bb-bacc-49cd-a9ee-6127bf369210","year":2019},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:a808c2cf51f8a9baf4611947e76f1d840d14c6f9340efa00db3ac74c32173970","observation_id":"1a343c59-1714-4e22-80b3-e90aeafcc86d","resolution":{"observed_at":"2026-05-14T12:55:21.508410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neural networks trained with sgd learn distributions of increasing complexity","venue":null,"work_id":"0cd143c8-42f2-4a9c-8870-01f68d0adc89","year":2023},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:ea86203115b780ad600c03a96c9d0289a73c46054fe0604a0ec80e680d5d3641","observation_id":"3c564f30-1595-4bfb-a601-e33fac787658","resolution":{"observed_at":"2026-05-14T12:55:21.504999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A distributional simplicity bias in the learning dynamics of transformers.Advances in Neural Information Processing Systems, 37:96207–96228","venue":null,"work_id":"8879a4f4-807c-4b4a-af90-8c2327c294ce","year":2024},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:a1bc33ed757a74955549973f1d8ccc9bdc744f0dc2e9e3c034430f91af1c4c11","observation_id":"3ffadbe6-2bfa-4888-acfc-c22d56627117","resolution":{"observed_at":"2026-05-14T12:55:21.501910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04362","last_updated":"2024-10-09T06:43:49Z","snapshot_observed_at":"2026-08-10T08:25:04.975544Z","submitted_at":"2024-02-06T20:03:35Z","title":"Neural Networks Learn Statistics of Increasing Complexity","version":3},"cited_work":{"arxiv_id":"2402.04362","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.04362","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"& Fern, X","venue":null,"work_id":"3803730c-4a5c-4f3e-aafc-3ff58e1860a6","year":2024},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"cited_paper":"/paper/2402.04362","citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:10fde4a6399e2d0cce32861f519ddf52579a302fff49d7f074abcc9d9125afbf","observation_id":"73e94549-0d15-4ebb-8a5e-730c7422e1d8","resolution":{"observed_at":"2026-05-11T02:25:53.721268Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15138","last_updated":"2025-06-10T08:51:44Z","snapshot_observed_at":"2026-08-07T08:59:10.821438Z","submitted_at":"2024-08-27T15:23:09Z","title":"How transformers learn structured data: insights from hierarchical filtering","version":3},"cited_work":{"arxiv_id":"2408.15138","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.15138","snapshot_observed_at":"2026-07-04T05:39:40.951218Z","title":"How transformers learn structured data: insights from hierarchical filtering","venue":null,"work_id":"6a812cd5-4071-4c58-a557-2185d65e2c8b","year":2024},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"cited_paper":"/paper/2408.15138","citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:a0038df233e5a00c57f69d7cd0d69374c826a7758133c96168128f08b2698d2e","observation_id":"da0f22af-ea31-4280-ba49-1e888a21c31e","resolution":{"observed_at":"2026-05-11T02:25:53.693368Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.05013","last_updated":"2024-02-07T16:32:29Z","snapshot_observed_at":"2026-07-06T17:26:55.484246Z","submitted_at":"2024-02-07T16:32:29Z","title":"Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth","version":1},"cited_work":{"arxiv_id":"2402.05013","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.05013","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Compression of structured data with autoencoders: Provable benefit of nonlinearities and depth","venue":null,"work_id":"db36b9a9-724a-480c-833e-ba27b5bcf60e","year":2024},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"cited_paper":"/paper/2402.05013","citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:7fd6ecd5a4b87c87a7bbdb34985f02e9698fdc771fee3a588cee476fca4d556d","observation_id":"3750bdbf-63ff-4677-829c-5752c6a83ba5","resolution":{"observed_at":"2026-05-11T02:25:53.698526Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Inferring effective couplings with restricted boltzmann machines.SciPost Physics, 16(4):095","venue":null,"work_id":"1b23b6f7-b947-4e2a-94aa-7cb7c176a464","year":2024},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:d8c206c984acc78fa1f243cb91f9d7daca752d51b00674a77fa76d7be28ea476","observation_id":"e4042118-de45-4db2-a7ea-bdf9fdaf2808","resolution":{"observed_at":"2026-05-14T12:55:21.577547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Inferring higher-order couplings with neural networks.Physical Review Letters, 135(20):207301","venue":null,"work_id":"f13ce8df-248f-46ea-9567-b604eb6a89f3","year":2025},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:d390ee4c4f01292c497fb639b58e3914cb045bccfc06120f9c0c092d8deb48a6","observation_id":"712fe6af-2c8a-47ef-807f-e52b4cf58436","resolution":{"observed_at":"2026-05-14T12:55:21.515838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.12089","last_updated":"2025-06-04T11:03:45Z","snapshot_observed_at":"2026-08-07T18:11:31.857137Z","submitted_at":"2025-02-17T18:06:33Z","title":"How Compositional Generalization and Creativity Improve as Diffusion Models are Trained","version":3},"cited_work":{"arxiv_id":"2502.12089","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.12089","snapshot_observed_at":"2026-07-03T00:17:29.059596Z","title":"& Wyart, M.How compositional generalization and creativity improve as diffusion models are traineden","venue":null,"work_id":"00d7f2d2-e8c4-49d2-be0d-79eb5e111b2a","year":2025},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"cited_paper":"/paper/2502.12089","citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:b157095bd0ceb8d227c3a0e02141c131bf040cd015b04009aa65e97a18881730","observation_id":"0719f44d-06c4-487c-bf2d-1c483407ba00","resolution":{"observed_at":"2026-05-11T02:25:53.711358Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2603.12901","last_updated":"2026-06-10T13:28:42Z","snapshot_observed_at":"2026-08-02T23:19:22.088855Z","submitted_at":"2026-03-13T11:07:01Z","title":"A theory of learning data statistics in diffusion models, from easy to hard","version":2},"cited_work":{"arxiv_id":"2603.12901","doi":null,"metadata_source":"pith","pith_arxiv_id":"2603.12901","snapshot_observed_at":"2026-07-10T01:26:43.190573Z","title":"A theory of learning data statistics in diffusion models, from easy to hard","venue":"stat.ML","work_id":"c96e450c-1787-470d-9bc8-8082eeba264e","year":2026},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"cited_paper":"/paper/2603.12901","citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:21cf95d93753989a81de190a781469b32fa7ba853fe36866d8b44b12110a930e","observation_id":"e9933c87-9769-4f12-91cd-2620a14f1c11","resolution":{"observed_at":"2026-05-21T03:03:42.220012Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-07-05T22:21:32.286149Z","title":"Exact solution for on-line learning in multilayer neural networks.Physical Review Letters, 74(21):4337","venue":null,"work_id":"f22fdd33-848e-415e-975f-11e378fd6358","year":1995},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:8dc3fee7b76161543a8185a6feae57a1d248cdcad00d10bb2dcf950eace8505d","observation_id":"47c96726-af1e-4b79-8357-c9c8241c7224","resolution":{"observed_at":"2026-05-14T12:55:21.529727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A mean field view of the landscape of two-layer neural networks.Proceedings of the National Academy of Sciences, 115(33):E7665–E7671","venue":null,"work_id":"ce1cbda9-bdba-417e-8b41-36f290f2e309","year":2018},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:912780605bcc8f11874711d32465cf37dff1d1b802af4c5d294e471684d0b9e2","observation_id":"77433117-fe8d-41f0-9327-ecedee3ab586","resolution":{"observed_at":"2026-05-14T12:55:21.526576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning protein constitutive motifs from sequence data","venue":null,"work_id":"6a9c9c31-d54b-40d3-9529-2ff7e5855a90","year":2019},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:3b58c1c73174c5871ff7c8ef20b9586975a330af21b87cdca318bb9de7bca70b","observation_id":"693b8660-2db3-42bc-abcf-500d7a905e71","resolution":{"observed_at":"2026-05-14T12:55:21.568448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2603.11032","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Uncovering statistical structure in large-scale neural activity with restricted boltzmann machines","venue":null,"work_id":"cc362ace-9194-48fd-9a91-d0cbe29a8af1","year":2026},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:ef3c47a5a530ea0c913e70b432b7781a1cf0068f9aa3e545c20685694cdee8fe","observation_id":"be1e64fc-4f30-46f2-bed1-04f1c6099848","resolution":{"observed_at":"2026-05-11T02:25:53.684119Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The loss surfaces of multilayer networks.Proceedings of AISTATS","venue":null,"work_id":"dc525d13-a0c0-43e7-993e-06a9d3d9a549","year":2015},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:2c301f72b7e4822a00f934291da660b78d161f8637720dd67ad3043e6ec7711b","observation_id":"ea321a84-2552-4663-a562-722a0d3e8ffe","resolution":{"observed_at":"2026-05-14T12:55:21.552306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Escaping from saddle points—online stochastic gradient for tensor decomposition","venue":null,"work_id":"ef47734d-5bab-47a5-b723-ad7ce5e95c81","year":2015},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:5f6df29d8e72f455cc4fa84615d2bcf38d0f6f36702be9cae6e510a8a59cda49","observation_id":"7201b81e-8bc0-4b0d-a482-37f8b5eab4e8","resolution":{"observed_at":"2026-05-14T12:55:21.555547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On nonconvex optimization for machine learning: Gradients, stochasticity, and saddle points.Journal of the ACM (JACM), 68(2):1–29","venue":null,"work_id":"560ae983-2721-4760-b777-e67db91a30b0","year":2021},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:e5a68bddaee95ee237ba5560be0f5ccefd6516b150a6634402391503adefeb28","observation_id":"349e0564-f881-4954-bace-f1c79f27f1c8","resolution":{"observed_at":"2026-05-14T12:55:21.564584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neural tangent kernel: Convergence and generalization in neural networks.Advances in neural information processing systems, 31","venue":null,"work_id":"27979986-f930-4921-95e2-f413015ff8e2","year":2018},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:c67156658def53a6f0fbc53526a8127cc4c561e0a7dd419a646523785caab7f2","observation_id":"428730f2-4439-498b-8b14-c9d0cf8cd1f9","resolution":{"observed_at":"2026-05-14T12:55:21.548747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit","venue":null,"work_id":"eb6fc752-f425-4cf4-b746-8b55d31ae885","year":2019},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:5abdbc7f4759111efcfc73247d3be7986b18eea13bb1b11bf26f1d62c0ae8e7c","observation_id":"370eb9fd-c254-4478-8d7b-d6c633561bb9","resolution":{"observed_at":"2026-05-14T12:55:21.519981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Implicit regularization in nonconvex statistical estimation: Gradient descent converges linearly for phase retrieval and matrix completion","venue":null,"work_id":"14950524-c804-4681-a5fb-e41999100870","year":2018},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:71738737f66d1c2e5f5c0851a2f8833a4f7b5bd09e8c9d3da91433b1fd8444fb","observation_id":"b6cc9a3d-6e60-4ac9-a1bb-47e26b121c4e","resolution":{"observed_at":"2026-05-14T12:55:21.580395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.03530","last_updated":"2017-02-26T19:36:40Z","snapshot_observed_at":"2026-08-01T16:56:59.989486Z","submitted_at":"2016-11-10T22:02:36Z","title":"Understanding deep learning requires rethinking generalization","version":2},"cited_work":{"arxiv_id":"1611.03530","doi":"10.48550/arxiv.1611.03530","metadata_source":"pith","pith_arxiv_id":"1611.03530","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Understanding deep learning requires rethinking generalization","venue":"cs.LG","work_id":"b260f96a-16f6-4936-8f3b-440917a19b34","year":2016},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"cited_paper":"/paper/1611.03530","citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:a0d333a6ecbbd63124cbbda818ecd82731e0ee10a71e7ce10cb33e1b7cc4073d","observation_id":"d42d4aa4-28c8-4cc4-861a-13220a3ab2fb","resolution":{"observed_at":"2026-05-13T11:56:40.372790Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-07-11T05:49:49.910618+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T05:49:49.910618+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Exact training of restricted boltzmann machines on intrinsically low dimensional data.Physical Review Letters, 127(15):158303","venue":null,"work_id":"c25e33ae-edf8-4466-b027-97e75af452c1","year":2021},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:50c41469279f86149a50be137fc5003457acb11ce17811ec5ea4080961ea3807","observation_id":"67d0ad11-298e-4c01-a774-610af1fe632e","resolution":{"observed_at":"2026-05-14T12:55:21.561512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On the anatomy of mcmc-based maximum likelihood learning of energy-based models.Proceedings of the AAAI Conference on Artificial Intelligence","venue":null,"work_id":"60a72bd9-099e-4fc0-9270-44aa4d35545d","year":2020},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:8407c628b62bcad34373f792a5ffda01019b187dc42561c0b6fd30edf4ad66ef","observation_id":"954ddfda-932f-4450-b792-a2808f410eea","resolution":{"observed_at":"2026-05-14T12:55:21.585903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Exact training of restricted boltzmann machines on intrinsically low- dimensional data.Physical Review Letters, 127:158303","venue":null,"work_id":"465d8814-4cf8-4c1c-9922-9f159aedab59","year":2021},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:15214bf3e401cdb97ebbb8d288eb1d8cb04251fdbd8b6c04dfcbb0a9a855a557","observation_id":"a4515460-735e-4a6b-889d-d4d9fc0e4b89","resolution":{"observed_at":"2026-05-14T12:55:21.583392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Explaining the effects of non- convergent MCMC in the training of energy-based models","venue":null,"work_id":"75f3ba6f-055d-47bc-9224-16a881b66b42","year":2023},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:77f6ef55f4b27589978266d039b36d0a3a82dd24589e238eead5f8c573a2d975","observation_id":"ed1d2e76-d61c-4656-84c3-d62ea0154604","resolution":{"observed_at":"2026-05-14T12:55:21.571593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Representational power of restricted boltzmann machines and deep belief networks.Neural computation, 20(6):1631–1649","venue":null,"work_id":"bd8c77eb-6bd9-4c0c-9c84-daabb829be77","year":2008},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:7a718f76a326de123512faf47033e4a1795cf6823d29e46aa9113c7b6dfb0767","observation_id":"fbf7e3b8-9cb6-47c5-8550-d8f3812daabb","resolution":{"observed_at":"2026-05-14T12:55:21.511993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Refinements of universal approximation results for deep belief networks and restricted boltzmann machines.Neural computation, 23(5):1306–1319","venue":null,"work_id":"8c68769d-1af8-4edc-a18f-fef8a343c4bd","year":2011},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:32eb0ffe98447f061ead95bda3fb33fbe8846777ae9424b007b87682bb81306d","observation_id":"9b934f0e-5c5a-48bb-9059-0de3348aef56","resolution":{"observed_at":"2026-05-14T12:55:21.558521Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Expressive power and approximation errors of restricted boltzmann machines.Advances in neural information processing systems, 24","venue":null,"work_id":"e79f433e-ec0f-4903-b1d2-8c19cf140734","year":2011},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:1f7729d361c07e832fee259b92ce4650f662b9661d1502e37f781bc5e0204ce7","observation_id":"aa1d1782-a238-46f1-8aba-93a1d5ff8065","resolution":{"observed_at":"2026-05-14T12:55:21.536759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Backpropagation applied to handwritten zip code recognition.Neural computation, 1(4):541– 551","venue":null,"work_id":"57752bcc-4e63-4bdb-a6c3-d16d50840a90","year":1989},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:0597f6ea1a119847bc1649ce92ce1cca358cea061b1a84fc35b647ee670ff116","observation_id":"e36f9728-0a4a-45cb-898a-00b933f6826d","resolution":{"observed_at":"2026-05-14T12:55:21.544311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neuropixels visual coding (dataset) 2019","venue":null,"work_id":"334f1a6e-8f16-48e8-8337-2b8ed75aabb5","year":2019},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:790c754dd6281880100519bb960f074704e14cb96ef915328b77ae409a8152df","observation_id":"965cc23c-d06b-49e0-a860-58a5fe0ec78a","resolution":{"observed_at":"2026-05-14T12:55:21.540920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cambridge University Press","venue":null,"work_id":"d0edba11-334c-41b7-8841-b1c45e9b848d","year":2014},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:8acf27384f12d2407d41349c6e4d912a1a6d581da180800f405039b7310c6fd5","observation_id":"dd4ea5ac-a71d-43d3-8f7d-e48e47f87ba8","resolution":{"observed_at":"2026-05-14T12:55:21.523235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mnist handwritten digit database","venue":null,"work_id":"06cd5595-d72b-4782-a539-af090439beb0","year":1998},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:1aaa8ec18c4a9f0cb994d638065fe6c2eea0e06d0cb9149910171d5ffec659a7","observation_id":"14f7622a-3b28-446f-ad6f-c4a7f204bfcb","resolution":{"observed_at":"2026-05-14T12:55:21.574595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2405.15376","doi":"10.48550/arxiv.2405.15376.url:","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Fast training and sampling of restricted boltzmann machines","venue":null,"work_id":"9c9f5b0b-7c92-4c41-a24e-7720715276df","year":2025},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:5cb2b7f9fbc8b224ebbcd60157ef40312e43a8e930c7cdeba11eeb3755a3af79","observation_id":"a5946b66-94d3-4c86-a461-a403246cb32d","resolution":{"observed_at":"2026-05-11T02:25:53.746777Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Training energy-based models with parallel trajectory tempering","venue":null,"work_id":"425b8ca9-8de1-4999-8e90-4bf9cc5d917c","year":2025},"citing_paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:42.639358Z"},"links":{"citing_paper":"/paper/2605.07844"},"observation_digest":"sha256:60c1975713eda184f8860ed04e7248c4fa5d708aeced3a0251eee8a765ff9af6","observation_id":"b01183e5-9e68-4c2c-bc3c-260adff5038a","resolution":{"observed_at":"2026-05-14T12:55:21.533174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.07844","last_updated":"2026-05-08T15:08:00Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T16:50:43.739313Z","submitted_at":"2026-05-08T15:08:00Z","title":"Distributional simplicity bias and effective convexity in Energy Based Models"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":0,"verified_exact":8,"verified_fuzzy":26},"total_outbound_references":35},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2605.07844."}