{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:ZEOT4GCQZWVGJQSEVWDBNNJTXJ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f8c99360b9017aee2de951fce8f1def836a1d6b55d103fb3803fe26beb6a2a86","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-11T07:11:28Z","title_canon_sha256":"d80b01b381030f730716c5ac5c60d87a6d6f62b299c0eb7cfc86092173e5e336"},"schema_version":"1.0","source":{"id":"1806.03836","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.03836","created_at":"2026-05-18T00:00:30Z"},{"alias_kind":"arxiv_version","alias_value":"1806.03836v4","created_at":"2026-05-18T00:00:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.03836","created_at":"2026-05-18T00:00:30Z"},{"alias_kind":"pith_short_12","alias_value":"ZEOT4GCQZWVG","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZEOT4GCQZWVGJQSE","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZEOT4GCQ","created_at":"2026-05-18T12:33:07Z"}],"graph_snapshots":[{"event_id":"sha256:7f364a239f61fe5a2833d89bb1facf2b83a0b30ace7c8f76d0e9e19ecfb23022","target":"graph","created_at":"2026-05-18T00:00:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem. In this paper, we propose a novel Bayesian model-agnostic meta-learning method. The proposed method combines scalable gradient-based meta-learning with nonparametric variational inference in a principled probabilistic framework. During fast adaptation, the method is capable of learning complex uncertainty structure beyond a point estimate or a simple Gaussian approximation. In addition, a robust Bayesian meta-update mechanism with ","authors_text":"Jaesik Yoon, Ousmane Dia, Sungjin Ahn, Sungwoong Kim, Taesup Kim, Yoshua Bengio","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-11T07:11:28Z","title":"Bayesian Model-Agnostic Meta-Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.03836","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:12074b4f7153b35ff84d35b6e041e03e483e3712809cba90c3cc39712073c9af","target":"record","created_at":"2026-05-18T00:00:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f8c99360b9017aee2de951fce8f1def836a1d6b55d103fb3803fe26beb6a2a86","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-11T07:11:28Z","title_canon_sha256":"d80b01b381030f730716c5ac5c60d87a6d6f62b299c0eb7cfc86092173e5e336"},"schema_version":"1.0","source":{"id":"1806.03836","kind":"arxiv","version":4}},"canonical_sha256":"c91d3e1850cdaa64c244ad8616b533ba502501d2e78ecab166d47a90471d3caa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c91d3e1850cdaa64c244ad8616b533ba502501d2e78ecab166d47a90471d3caa","first_computed_at":"2026-05-18T00:00:30.182242Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:30.182242Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ekO+FFdJUhupDsCVfgYnA47lSWn5dVQKwk2QykGDS8w8JZGO3vFG4VhFczbZLXt/slZN9JV6M+KipwoKzlFJCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:30.182722Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.03836","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:12074b4f7153b35ff84d35b6e041e03e483e3712809cba90c3cc39712073c9af","sha256:7f364a239f61fe5a2833d89bb1facf2b83a0b30ace7c8f76d0e9e19ecfb23022"],"state_sha256":"7d706c6c9c4aaef6df683d9c2e91b10afea4100650b9befda8c9eefbd0a1d715"}