{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CWISTD55I63YW2ITTK7YQNOER2","short_pith_number":"pith:CWISTD55","schema_version":"1.0","canonical_sha256":"1591298fbd47b78b69139abf8835c48eae695e82d948f1a102ae0ffe2865fa2b","source":{"kind":"arxiv","id":"2406.07107","version":3},"attestation_state":"computed","paper":{"title":"Agnostic Sharpness-Aware Minimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dinh Phung, Quyen Tran, Thanh-Toan Do, Trung Le, Tuan Truong, Van-Anh Nguyen","submitted_at":"2024-06-11T09:49:00Z","abstract_excerpt":"Sharpness-aware minimization (SAM) has been instrumental in improving deep neural network training by minimizing both the training loss and the sharpness of the loss landscape, leading the model into flatter minima that are associated with better generalization properties. In another aspect, Model-Agnostic Meta-Learning (MAML) is a framework designed to improve the adaptability of models. MAML optimizes a set of meta-models that are specifically tailored for quick adaptation to multiple tasks with minimal fine-tuning steps and can generalize well with limited data. In this work, we explore the"},"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":"2406.07107","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-11T09:49:00Z","cross_cats_sorted":[],"title_canon_sha256":"93ff05366da5d8ce7725ada331ebc01a3faa7e72f981edff56467d6fd36b1975","abstract_canon_sha256":"481ec95a9ce2e5007a39f255ef40388f1f405052b735945485005861564f3d7e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:14:34.132088Z","signature_b64":"ecay/uheXQ4hDUehUR7j0x2WnVxBVgFqszXvPr/HBqTzJcpfqO9EarJ4/jxAQ898J61+rjoqQeZSDJag76biCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1591298fbd47b78b69139abf8835c48eae695e82d948f1a102ae0ffe2865fa2b","last_reissued_at":"2026-07-05T09:14:34.131611Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:14:34.131611Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Agnostic Sharpness-Aware Minimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dinh Phung, Quyen Tran, Thanh-Toan Do, Trung Le, Tuan Truong, Van-Anh Nguyen","submitted_at":"2024-06-11T09:49:00Z","abstract_excerpt":"Sharpness-aware minimization (SAM) has been instrumental in improving deep neural network training by minimizing both the training loss and the sharpness of the loss landscape, leading the model into flatter minima that are associated with better generalization properties. In another aspect, Model-Agnostic Meta-Learning (MAML) is a framework designed to improve the adaptability of models. MAML optimizes a set of meta-models that are specifically tailored for quick adaptation to multiple tasks with minimal fine-tuning steps and can generalize well with limited data. In this work, we explore the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.07107","kind":"arxiv","version":3},"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/2406.07107/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":"2406.07107","created_at":"2026-07-05T09:14:34.131667+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.07107v3","created_at":"2026-07-05T09:14:34.131667+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.07107","created_at":"2026-07-05T09:14:34.131667+00:00"},{"alias_kind":"pith_short_12","alias_value":"CWISTD55I63Y","created_at":"2026-07-05T09:14:34.131667+00:00"},{"alias_kind":"pith_short_16","alias_value":"CWISTD55I63YW2IT","created_at":"2026-07-05T09:14:34.131667+00:00"},{"alias_kind":"pith_short_8","alias_value":"CWISTD55","created_at":"2026-07-05T09:14:34.131667+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/CWISTD55I63YW2ITTK7YQNOER2","json":"https://pith.science/pith/CWISTD55I63YW2ITTK7YQNOER2.json","graph_json":"https://pith.science/api/pith-number/CWISTD55I63YW2ITTK7YQNOER2/graph.json","events_json":"https://pith.science/api/pith-number/CWISTD55I63YW2ITTK7YQNOER2/events.json","paper":"https://pith.science/paper/CWISTD55"},"agent_actions":{"view_html":"https://pith.science/pith/CWISTD55I63YW2ITTK7YQNOER2","download_json":"https://pith.science/pith/CWISTD55I63YW2ITTK7YQNOER2.json","view_paper":"https://pith.science/paper/CWISTD55","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.07107&json=true","fetch_graph":"https://pith.science/api/pith-number/CWISTD55I63YW2ITTK7YQNOER2/graph.json","fetch_events":"https://pith.science/api/pith-number/CWISTD55I63YW2ITTK7YQNOER2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CWISTD55I63YW2ITTK7YQNOER2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CWISTD55I63YW2ITTK7YQNOER2/action/storage_attestation","attest_author":"https://pith.science/pith/CWISTD55I63YW2ITTK7YQNOER2/action/author_attestation","sign_citation":"https://pith.science/pith/CWISTD55I63YW2ITTK7YQNOER2/action/citation_signature","submit_replication":"https://pith.science/pith/CWISTD55I63YW2ITTK7YQNOER2/action/replication_record"}},"created_at":"2026-07-05T09:14:34.131667+00:00","updated_at":"2026-07-05T09:14:34.131667+00:00"}