{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RNOBISOESL75SMVSGBY22DSXOF","short_pith_number":"pith:RNOBISOE","schema_version":"1.0","canonical_sha256":"8b5c1449c492ffd932b23071ad0e5771438f7e5819faa0d3fa7c3d46e2ff7cf4","source":{"kind":"arxiv","id":"2407.06765","version":2},"attestation_state":"computed","paper":{"title":"A Generalization Bound for Nearly-Linear Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Eugene Golikov","submitted_at":"2024-07-09T11:20:01Z","abstract_excerpt":"We consider nonlinear networks as perturbations of linear ones. Based on this approach, we present novel generalization bounds that become non-vacuous for networks that are close to being linear. The main advantage over the previous works which propose non-vacuous generalization bounds is that our bounds are a-priori: performing the actual training is not required for evaluating the bounds. To the best of our knowledge, they are the first non-vacuous generalization bounds for neural nets possessing this property."},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2407.06765","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-09T11:20:01Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"451283306d177c6dc9884f67dd9aa0e45e1f96b16d5128f0f100df7359e409fe","abstract_canon_sha256":"1088cf87bab6267611e63c168f71f37051113c40773ff8931bf209b8a5b88f25"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T03:13:43.566111Z","signature_b64":"zFKp7jkNbKyJjtDzmUHPO4LosA0Di1N7rFjpoPxz1PmKrm5W7gUJnUw8mFtyAD+5xuM+A14DOPFYLHZLPOYOAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b5c1449c492ffd932b23071ad0e5771438f7e5819faa0d3fa7c3d46e2ff7cf4","last_reissued_at":"2026-06-23T03:13:43.565669Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T03:13:43.565669Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Generalization Bound for Nearly-Linear Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Eugene Golikov","submitted_at":"2024-07-09T11:20:01Z","abstract_excerpt":"We consider nonlinear networks as perturbations of linear ones. Based on this approach, we present novel generalization bounds that become non-vacuous for networks that are close to being linear. The main advantage over the previous works which propose non-vacuous generalization bounds is that our bounds are a-priori: performing the actual training is not required for evaluating the bounds. To the best of our knowledge, they are the first non-vacuous generalization bounds for neural nets possessing this property."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.06765","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2407.06765/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2407.06765","created_at":"2026-06-23T03:13:43.565724+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.06765v2","created_at":"2026-06-23T03:13:43.565724+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.06765","created_at":"2026-06-23T03:13:43.565724+00:00"},{"alias_kind":"pith_short_12","alias_value":"RNOBISOESL75","created_at":"2026-06-23T03:13:43.565724+00:00"},{"alias_kind":"pith_short_16","alias_value":"RNOBISOESL75SMVS","created_at":"2026-06-23T03:13:43.565724+00:00"},{"alias_kind":"pith_short_8","alias_value":"RNOBISOE","created_at":"2026-06-23T03:13:43.565724+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RNOBISOESL75SMVSGBY22DSXOF","json":"https://pith.science/pith/RNOBISOESL75SMVSGBY22DSXOF.json","graph_json":"https://pith.science/api/pith-number/RNOBISOESL75SMVSGBY22DSXOF/graph.json","events_json":"https://pith.science/api/pith-number/RNOBISOESL75SMVSGBY22DSXOF/events.json","paper":"https://pith.science/paper/RNOBISOE"},"agent_actions":{"view_html":"https://pith.science/pith/RNOBISOESL75SMVSGBY22DSXOF","download_json":"https://pith.science/pith/RNOBISOESL75SMVSGBY22DSXOF.json","view_paper":"https://pith.science/paper/RNOBISOE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.06765&json=true","fetch_graph":"https://pith.science/api/pith-number/RNOBISOESL75SMVSGBY22DSXOF/graph.json","fetch_events":"https://pith.science/api/pith-number/RNOBISOESL75SMVSGBY22DSXOF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RNOBISOESL75SMVSGBY22DSXOF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RNOBISOESL75SMVSGBY22DSXOF/action/storage_attestation","attest_author":"https://pith.science/pith/RNOBISOESL75SMVSGBY22DSXOF/action/author_attestation","sign_citation":"https://pith.science/pith/RNOBISOESL75SMVSGBY22DSXOF/action/citation_signature","submit_replication":"https://pith.science/pith/RNOBISOESL75SMVSGBY22DSXOF/action/replication_record"}},"created_at":"2026-06-23T03:13:43.565724+00:00","updated_at":"2026-06-23T03:13:43.565724+00:00"}