{"as_of":"2026-08-07T12:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d950aa905fe6c77f65c83689b377749e4ea21c692ca30cd2e02f0beef62f7a80","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":38,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":38,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":38,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:22:25.087828Z","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":"2026-07-04T08:19:44.369596Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2010.01412","last_updated":"2021-04-29T16:44:25Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-03T19:02:10Z","title":"Sharpness-Aware Minimization for Efficiently Improving Generalization","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-16T20:13:53.366151Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2010.01412"},"observation_digest":"sha256:7c4e82171a77f94912f4573a06986badbf5c6671460a352227f590dd8a3dd3af","observation_id":"f1fd38b3-1818-4e7a-b643-102c805729fc","resolution":{"observed_at":"2026-05-16T20:13:53.467732Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2102.11840","last_updated":"2026-05-19T16:23:20Z","snapshot_observed_at":"2026-07-06T10:43:58.764226Z","submitted_at":"2021-02-23T18:17:47Z","title":"Convergence rates for gradient descent in the training of overparameterized artificial neural networks with piecewise affine activation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-24T14:10:07.099631Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2102.11840"},"observation_digest":"sha256:be462fefc2f83ae0de4c9b40abb4231db1218a8787c4ce773302399ce751d459","observation_id":"1f683a74-bdfe-446f-a7c4-6fdef0c8efde","resolution":{"observed_at":"2026-05-24T14:14:33.318859Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2212.04089","last_updated":"2023-03-31T15:27:01Z","snapshot_observed_at":"2026-08-03T22:12:27.993418Z","submitted_at":"2022-12-08T05:50:53Z","title":"Editing Models with Task Arithmetic","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-13T08:09:12.716163Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2212.04089"},"observation_digest":"sha256:81c28b6e68284d836a7660c8ba216f5a5af4967db55f534e9d9f78ec5b28b662","observation_id":"5caa8a75-693a-4010-8e18-715d7dbc41d0","resolution":{"observed_at":"2026-05-13T08:09:13.010393Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2212.08989","last_updated":"2023-06-20T01:01:34Z","snapshot_observed_at":"2026-08-02T16:18:30.860460Z","submitted_at":"2022-12-18T02:03:00Z","title":"Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics","version":3},"reference_index":234,"source":"pdf_text","source_observed_at":"2026-05-24T10:22:00.419523Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2212.08989"},"observation_digest":"sha256:ae0547d5d37650c3d2959bcd9c49295548164f531336ac4fb63f8b93fd3aa493","observation_id":"86ddc345-4824-4989-8860-15b13613e0b3","resolution":{"observed_at":"2026-05-24T10:24:20.288721Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2505.17907","last_updated":"2026-05-12T14:58:30Z","snapshot_observed_at":"2026-07-06T21:29:16.251954Z","submitted_at":"2025-05-23T13:53:02Z","title":"Approximating Simple ReLU Networks based on Spectral Decomposition of Fisher Information","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-19T13:51:18.629183Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2505.17907"},"observation_digest":"sha256:9b73b0aeea25d0b6968466622b658ea32017581f9d7bf308655f1649f85d8f1c","observation_id":"e6d34117-bbce-48b5-a26b-612c62d25232","resolution":{"observed_at":"2026-05-19T13:52:19.834658Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-08-07T05:22:25.087828Z","title":"Neural tangent kernel: Convergence and generalization in neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.08417","last_updated":"2025-06-10T03:43:22Z","snapshot_observed_at":"2026-08-07T05:09:28.766545Z","submitted_at":"2025-06-10T03:43:22Z","title":"Offline RL with Smooth OOD Generalization in Convex Hull and its Neighborhood","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T05:22:25.087828Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2506.08417"},"observation_digest":"sha256:20ff568fdfd27527fb372f4447e166512229a75975cb7e9a1dbcbb1eb9f0b45a","observation_id":"bb277ef5-c440-4243-8801-ae2ca4e47a57","resolution":{"observed_at":"2026-08-07T05:22:25.087828Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-08-07T01:00:28.392251Z","title":"Jacot, F","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.12230","last_updated":"2025-06-13T21:17:50Z","snapshot_observed_at":"2026-08-07T03:44:45.546701Z","submitted_at":"2025-06-13T21:17:50Z","title":"Statistical Machine Learning for Astronomy -- A Textbook","version":1},"reference_index":1990,"source":"pdf_text","source_observed_at":"2026-08-07T01:00:28.392251Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2506.12230"},"observation_digest":"sha256:026e67bc76bf3c0b03109acd70d73694dda51d93a24909da7096630eb4774b7d","observation_id":"cc533bba-8cae-4f4e-b5d9-87b473d8b770","resolution":{"observed_at":"2026-08-07T01:00:28.392251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-08-05T20:29:39.738274Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.10490","last_updated":"2025-08-14T09:49:07Z","snapshot_observed_at":"2026-08-06T03:16:15.167041Z","submitted_at":"2025-08-14T09:49:07Z","title":"On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:39.738274Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2508.10490"},"observation_digest":"sha256:8b657c64b951e325af1d690d8c76ccbed02311bb150a530ead80e7f3d77cee6b","observation_id":"d2825434-fe74-4342-8f99-9a8cc4cfd15d","resolution":{"observed_at":"2026-08-05T20:29:39.738274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-08-05T15:53:38.631294Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.19437","last_updated":"2026-07-10T12:38:50Z","snapshot_observed_at":"2026-08-05T15:53:37.551248Z","submitted_at":"2025-08-26T21:14:52Z","title":"Is data-efficient learning feasible with quantum models?","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T15:53:38.631294Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2508.19437"},"observation_digest":"sha256:56eaf2e1aa01b7c3476ecddd2104fe7778c0a0affd00ae6de756d667639053d0","observation_id":"fccd0033-7d86-4292-bf42-71a8a9a0efed","resolution":{"observed_at":"2026-08-05T15:53:38.631294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-08-04T12:43:34.976985Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks, February 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.02872","last_updated":"2026-06-24T20:09:12Z","snapshot_observed_at":"2026-08-07T05:23:04.528575Z","submitted_at":"2025-10-03T10:18:49Z","title":"A physics-informed neural network approach to the point defect model for electrochemical oxide film growth","version":4},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T12:43:34.976985Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2510.02872"},"observation_digest":"sha256:bd0514aa17320a4702f6f86b22ef673a98846cb0eb18f79e2f7763f109ebe233","observation_id":"e56c2682-3414-43fd-8381-1e6a5cee7448","resolution":{"observed_at":"2026-08-04T12:43:34.976985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-08-03T13:34:53.574058Z","title":"Jacot, F","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.24116","last_updated":"2026-07-01T15:57:21Z","snapshot_observed_at":"2026-08-04T01:41:29.622604Z","submitted_at":"2025-12-30T09:53:02Z","title":"Quantitative Understanding of PDF Fits and their Uncertainties","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T13:34:53.574058Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2512.24116"},"observation_digest":"sha256:d970b6c4d44053aaf5f3ac3875210eeba9c9d0b0e6dbcbf7420f545ee8d3113b","observation_id":"b15c8ab1-15bc-47e7-a01c-fba8cd366172","resolution":{"observed_at":"2026-08-03T13:34:53.574058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-08-02T20:37:00.343884Z","title":"Neural tangent kernel: Convergence and generalization in neural networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.23039","last_updated":"2026-06-01T09:29:09Z","snapshot_observed_at":"2026-08-04T13:27:32.737495Z","submitted_at":"2026-02-26T14:24:11Z","title":"Dynamics of neural scaling laws in random feature regression with powerlaw-distributed kernel eigenvalues","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-02T20:37:00.343884Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2602.23039"},"observation_digest":"sha256:cdd9fc35b411375ab01dcfd5f52c13fc84783b2c612d496af619b26826d9b384","observation_id":"78ed4308-ef10-4e53-a908-8a759237ad06","resolution":{"observed_at":"2026-08-02T20:37:00.343884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2604.04655","last_updated":"2026-04-06T13:05:27Z","snapshot_observed_at":"2026-07-06T22:53:33.177713Z","submitted_at":"2026-04-06T13:05:27Z","title":"Grokking as Dimensional Phase Transition in Neural Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-10T19:34:03.055921Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2604.04655"},"observation_digest":"sha256:298dd59bb19c10a7cb6366c788cd61c99d5777aa6e9e1b75de19f18aebe5b9d9","observation_id":"1c5ccc4e-571d-42a9-ba7d-0a2804e91642","resolution":{"observed_at":"2026-05-10T22:45:51.160477Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2604.16431","last_updated":"2026-04-06T13:43:20Z","snapshot_observed_at":"2026-07-06T23:03:43.854612Z","submitted_at":"2026-04-06T13:43:20Z","title":"Dimensional Criticality at Grokking Across MLPs and Transformers","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T19:23:20.777796Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2604.16431"},"observation_digest":"sha256:14ad7689f8df878a76802f06b6190f9d2e5890523fb52f43fedce4701c07f967","observation_id":"3faff8f4-65c9-4fab-bd93-4deff9ed1b2e","resolution":{"observed_at":"2026-05-10T23:00:50.590833Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2604.18673","last_updated":"2026-07-15T15:27:18Z","snapshot_observed_at":"2026-08-02T15:55:36.020850Z","submitted_at":"2026-04-20T18:00:00Z","title":"Neural Networks Reveal a Universal Bias in Conformal Correlators","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-10T04:30:31.522758Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2604.18673"},"observation_digest":"sha256:cd43ae1d828d6813f69bde64585615afb4c36916d3d0882f7c16279d95aaf3f6","observation_id":"2b322821-036e-4035-a99d-a325528346a6","resolution":{"observed_at":"2026-05-11T11:51:04.280096Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-08-02T15:55:38.507583Z","title":"Jacot, F","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2604.18673","last_updated":"2026-07-15T15:27:18Z","snapshot_observed_at":"2026-08-02T15:55:36.020850Z","submitted_at":"2026-04-20T18:00:00Z","title":"Neural Networks Reveal a Universal Bias in Conformal Correlators","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-02T15:55:38.507583Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2604.18673"},"observation_digest":"sha256:be8dbd071aced37c09a9557c3b82ba6f00acf6d57505d37c3588b633371fa03a","observation_id":"c506e47a-6af2-4996-a521-bdcdf67c9f6d","resolution":{"observed_at":"2026-08-02T15:55:38.507583Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2604.18686","last_updated":"2026-07-27T11:49:19Z","snapshot_observed_at":"2026-08-03T17:24:23.472562Z","submitted_at":"2026-04-20T18:00:02Z","title":"Neural Spectral Bias and Conformal Correlators I: Introduction and Applications","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-10T04:03:19.633365Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2604.18686"},"observation_digest":"sha256:75fef4d765bc8b3fe4cce6af6f9f6a3030dff2575abe129308ba1c7a66b9f6d6","observation_id":"3af1aec0-2660-43d9-873a-d2249cfb101f","resolution":{"observed_at":"2026-05-11T12:11:08.714666Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-08-02T15:54:45.778777Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.18686","last_updated":"2026-07-27T11:49:19Z","snapshot_observed_at":"2026-08-03T17:24:23.472562Z","submitted_at":"2026-04-20T18:00:02Z","title":"Neural Spectral Bias and Conformal Correlators I: Introduction and Applications","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T15:54:45.778777Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2604.18686"},"observation_digest":"sha256:0240f83b2c90648ef59734956326593316661e050f58f1c1b672fe413a773e81","observation_id":"2e8a9583-7c73-45a9-aac2-e017e4fe35a5","resolution":{"observed_at":"2026-08-02T15:54:45.778777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2605.06563","last_updated":"2026-05-07T16:57:59Z","snapshot_observed_at":"2026-07-31T20:57:03.610204Z","submitted_at":"2026-05-07T16:57:59Z","title":"Criticality and Saturation in Orthogonal Neural Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-08T12:25:37.004451Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2605.06563"},"observation_digest":"sha256:29880a871a502b0489b27c7336e7458202ce2406f905fc18694204148f1a6cf6","observation_id":"e0cfce99-3a19-4a9d-99ad-6fc527d6e9c0","resolution":{"observed_at":"2026-05-11T19:16:08.978484Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2605.13160","last_updated":"2026-05-13T08:24:55Z","snapshot_observed_at":"2026-07-06T23:24:47.309044Z","submitted_at":"2026-05-13T08:24:55Z","title":"Kernel-based guarantees for nonlinear parametric models in Bayesian optimization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-14T18:04:54.310991Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2605.13160"},"observation_digest":"sha256:720a4b9b6ebfab4ad60538076d15fea0df48dac4c4838840b3c3050c8e54c116","observation_id":"5dd5f21c-124b-420d-83d4-f370e2cea5f5","resolution":{"observed_at":"2026-05-14T18:07:33.806411Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2605.13183","last_updated":"2026-05-13T08:42:40Z","snapshot_observed_at":"2026-08-07T01:17:51.388795Z","submitted_at":"2026-05-13T08:42:40Z","title":"Neural Networks, Dispersion Relations and the Thermal Bootstrap","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-14T18:49:27.729849Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2605.13183"},"observation_digest":"sha256:91290655cff393a1545afc22ef85c6acc522cd177f43420bf9684eace5b609c2","observation_id":"4e82a4c9-afaf-4f05-8070-e8834a97c9f2","resolution":{"observed_at":"2026-05-14T18:52:36.026297Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2605.13788","last_updated":"2026-05-15T16:53:38Z","snapshot_observed_at":"2026-08-02T05:09:13.977740Z","submitted_at":"2026-05-13T17:08:37Z","title":"Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-14T19:11:32.991952Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2605.13788"},"observation_digest":"sha256:583d187bebbc1e74fe257d263617b25f772002f96fdbee01dae6553a10025e9a","observation_id":"1afa586e-9080-4c59-a06b-a7a8b87790c9","resolution":{"observed_at":"2026-05-14T19:12:50.697558Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2605.13788","last_updated":"2026-05-15T16:53:38Z","snapshot_observed_at":"2026-08-02T05:09:13.977740Z","submitted_at":"2026-05-13T17:08:37Z","title":"Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-19T16:45:31.705747Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2605.13788"},"observation_digest":"sha256:92b662a3ce9aaf6f2fb2969a8637b5a790d06badd48039c3f7896de0c8bd0e93","observation_id":"e4a971ec-54ff-429a-954e-6fe1c2390656","resolution":{"observed_at":"2026-05-19T16:47:40.322373Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2605.16622","last_updated":"2026-05-15T20:43:26Z","snapshot_observed_at":"2026-08-01T15:07:39.814716Z","submitted_at":"2026-05-15T20:43:26Z","title":"Does Weight Decay Enhance Training Stability?","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-20T19:49:01.351717Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2605.16622"},"observation_digest":"sha256:ecbd3f6e52ad55646c31fbe30d142f767836624e3395f439b73ef5e85d96ce8c","observation_id":"ede0aa0d-9530-4a8e-a0b5-67c92bae450c","resolution":{"observed_at":"2026-05-20T19:53:42.980271Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2605.19195","last_updated":"2026-05-18T23:51:02Z","snapshot_observed_at":"2026-07-06T23:29:57.003383Z","submitted_at":"2026-05-18T23:51:02Z","title":"The Thermodynamic Costs of Simple Linear Regression","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-05-20T07:03:21.987679Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2605.19195"},"observation_digest":"sha256:9d8be18c761b1d2f130dbceac0e194e2c52be4aef8328154ea58ad6d5ab7d51d","observation_id":"007e9b3f-6f2b-45c3-b1b6-434950cb6843","resolution":{"observed_at":"2026-05-20T07:03:23.223530Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2605.22115","last_updated":"2026-05-21T07:47:35Z","snapshot_observed_at":"2026-07-06T23:32:30.578213Z","submitted_at":"2026-05-21T07:47:35Z","title":"Physics-Informed Neural Networks with Attention Feature Expansion for Monge-Amp\\`ere Equations","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-22T04:15:15.399040Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2605.22115"},"observation_digest":"sha256:d1cf384f8f1675b2b47afca2394fc84e9d3b38ad5ea7b45a022b09c31bb002f0","observation_id":"58750b17-8132-4e6d-91c2-af1932145e95","resolution":{"observed_at":"2026-05-22T04:16:02.663511Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2605.28585","last_updated":"2026-05-27T15:09:02Z","snapshot_observed_at":"2026-08-07T06:12:39.413721Z","submitted_at":"2026-05-27T15:09:02Z","title":"Outer-Momentum Restarting in High-Dimensional Two-Phase Optimization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T13:31:23.664332Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2605.28585"},"observation_digest":"sha256:74c3841463e7e3c25c1275ddfeb7b8dd441f2db1ba7dbe3af987b8f76adf097b","observation_id":"e70c5904-f52d-46ee-af9a-19fe406557a2","resolution":{"observed_at":"2026-06-29T13:33:27.934329Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2605.30860","last_updated":"2026-05-29T05:33:41Z","snapshot_observed_at":"2026-07-06T23:40:07.833692Z","submitted_at":"2026-05-29T05:33:41Z","title":"Bayesian Inference with Shaped Deep Non-linear MLPs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-28T20:41:59.878583Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2605.30860"},"observation_digest":"sha256:9f540b29fad90ca83496476121d09237694b621a6185c2c4c99d5108c9c98bd9","observation_id":"1c52ad05-ebf9-4808-aa6f-22d8139600ba","resolution":{"observed_at":"2026-06-28T20:42:37.073153Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2606.07247","last_updated":"2026-06-11T15:21:35Z","snapshot_observed_at":"2026-07-31T23:52:01.520202Z","submitted_at":"2026-06-05T13:17:18Z","title":"Theory of learning of high-dimensional controlled non-linear dynamical systems (I): models and methods","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-27T20:24:20.475551Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2606.07247"},"observation_digest":"sha256:ce5f0a04fa3933da141e53d96fcefef66a7ddb5214b8446120842693568fe024","observation_id":"debd0045-89d5-4fb5-a0e1-ef77fb5eb61e","resolution":{"observed_at":"2026-07-02T20:27:22.503405Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2606.08316","last_updated":"2026-06-06T20:01:31Z","snapshot_observed_at":"2026-08-06T03:58:16.304015Z","submitted_at":"2026-06-06T20:01:31Z","title":"Some Inverse Problems in Particle Physics","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-27T18:38:49.963266Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2606.08316"},"observation_digest":"sha256:3262d26f0465852c5af4457d9bc423b1387d3867b9d32a8c97c61f25b3ed8221","observation_id":"e5636e25-32d9-4f3d-a53f-cde8d32b0deb","resolution":{"observed_at":"2026-07-02T22:47:26.166481Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2606.09950","last_updated":"2026-06-08T08:57:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-06-08T08:57:34Z","title":"Integrating Out, Twice:The Open-System Case That Neural-Network Ensemble Theory Is Missing","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-27T17:13:23.663885Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2606.09950"},"observation_digest":"sha256:7c8fc87e13b818a79b2d82a21998bd63fc519b98c53b4d031a1af18b33c30d91","observation_id":"a1ab768a-c8cb-4402-bdcd-cdff47b49baa","resolution":{"observed_at":"2026-07-03T00:27:29.658134Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2606.11317","last_updated":"2026-06-09T18:01:42Z","snapshot_observed_at":"2026-08-07T02:30:56.163449Z","submitted_at":"2026-06-09T18:01:42Z","title":"Lectures on Semiclassical Methods for Composite Operators","version":1},"reference_index":117,"source":"pdf_text","source_observed_at":"2026-06-27T12:08:41.132334Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2606.11317"},"observation_digest":"sha256:34ccca5f501793156c435728b3673ae5fe8641455523b0172cb1fa788e4f2667","observation_id":"9b9574f5-91c4-4176-a72d-1e08e9e0743d","resolution":{"observed_at":"2026-07-03T07:27:43.985747Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2606.11319","last_updated":"2026-06-09T18:02:09Z","snapshot_observed_at":"2026-08-02T22:31:36.533257Z","submitted_at":"2026-06-09T18:02:09Z","title":"Learning from almost nothing: How neural networks survive heavy input corruption","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-27T14:06:36.969337Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2606.11319"},"observation_digest":"sha256:dceb611c1715f2bb44814762ce314c537df875dfd98b1221ebaa996f664c8949","observation_id":"9d48b0a6-13a2-423e-aec0-a02c9f658168","resolution":{"observed_at":"2026-07-03T04:07:37.268654Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":"1806.07572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-04T08:19:44.369596Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","venue":null,"work_id":"9dee0f55-0834-4719-91a5-5fa4fbd6f87b","year":2018},"citing_paper":{"arxiv_id":"2606.22019","last_updated":"2026-06-20T12:48:31Z","snapshot_observed_at":"2026-08-06T19:21:18.734073Z","submitted_at":"2026-06-20T12:48:31Z","title":"Channel Location Constrains the Auditability of Subliminal Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-26T11:52:03.948568Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2606.22019"},"observation_digest":"sha256:c256c24899981f83f35d682f96211f08841dfc87ef1fd09819aa521366606c32","observation_id":"3a9ad954-90cc-46a7-8ab5-15f0b139e807","resolution":{"observed_at":"2026-07-04T08:19:44.371546Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-07-12T06:14:03.658427Z","title":"Jacot, F","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.02905","last_updated":"2026-07-03T03:04:12Z","snapshot_observed_at":"2026-08-05T19:09:36.089911Z","submitted_at":"2026-07-03T03:04:12Z","title":"Pre-Strings Lectures on Artificial Intelligence","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-12T06:14:03.658427Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2607.02905"},"observation_digest":"sha256:9c37f32e680ab774c275a78828432dd8406fc28a22ba0bfab5784c4a2b85421a","observation_id":"7bae33de-727d-495a-af30-c8fc7a6cee01","resolution":{"observed_at":"2026-07-12T06:14:03.658427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-08-02T03:55:34.829928Z","title":"Neural tangent kernel: Convergence and generalization in neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.13749","last_updated":"2026-07-15T12:04:43Z","snapshot_observed_at":"2026-08-07T09:29:26.080200Z","submitted_at":"2026-07-15T12:04:43Z","title":"Algebraic Representability as the Limiting Regime of Grokking: An Exactly Solvable Model with Holomorphic Activations","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T03:55:34.829928Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2607.13749"},"observation_digest":"sha256:01d39f181f3694e531a92c78ff1eb6130a5104756d3fb4dfc734b6fbe2da1c03","observation_id":"7ee5fc7b-1515-4c0e-9e75-e1db86041400","resolution":{"observed_at":"2026-08-02T03:55:34.829928Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-08-02T02:49:58.203333Z","title":"Jacot, F","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.14216","last_updated":"2026-07-15T18:00:05Z","snapshot_observed_at":"2026-08-06T05:00:33.818947Z","submitted_at":"2026-07-15T18:00:05Z","title":"Coupled by Design: Computing Kerr-Newman Quasinormal Modes with a Hybrid SpectralPINN Solver","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-02T02:49:58.203333Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2607.14216"},"observation_digest":"sha256:c393e4336570c2bb15cc840156e88c3f35878f6d6e4ddc5dad1c84ce2c52cda2","observation_id":"cb99f5a3-e211-4777-b34e-06028ab386fc","resolution":{"observed_at":"2026-08-02T02:49:58.203333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-08-01T06:08:26.977225Z","title":"Available: https://arxiv.org/abs/1806.07572","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22010","last_updated":"2026-07-24T06:15:39Z","snapshot_observed_at":"2026-08-04T11:43:46.786247Z","submitted_at":"2026-07-24T06:15:39Z","title":"How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-01T06:08:26.977225Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2607.22010"},"observation_digest":"sha256:c1222bad08fd1246af756c6c2702a1a1fe100ff00d49eea231b9b8c046b40f80","observation_id":"6efd4d0f-16af-4acb-a937-54ddfebc85c9","resolution":{"observed_at":"2026-08-01T06:08:26.977225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1806.07572/citation-record","integrity":"/paper/1806.07572/integrity","json":"/paper/1806.07572/citation-record.json","paper":"/paper/1806.07572"},"outbound":[],"paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-30T19:50:14.167277Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 38 inbound Pith citation observations for arXiv:1806.07572."}