{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:DWP63CKBSVLIWL2NGT3CPFRRB4","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":"48ef6b344669a7d5c6cabe2186278e4fe44b1874fe6e7a85925529e37c4d4132","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-31T12:31:21Z","title_canon_sha256":"a1b75ec36a3da2324c505d00a0c638a0ec11881b2d9896ae31d995b3a3ece609"},"schema_version":"1.0","source":{"id":"2205.15745","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.15745","created_at":"2026-07-05T08:40:51Z"},{"alias_kind":"arxiv_version","alias_value":"2205.15745v3","created_at":"2026-07-05T08:40:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.15745","created_at":"2026-07-05T08:40:51Z"},{"alias_kind":"pith_short_12","alias_value":"DWP63CKBSVLI","created_at":"2026-07-05T08:40:51Z"},{"alias_kind":"pith_short_16","alias_value":"DWP63CKBSVLIWL2N","created_at":"2026-07-05T08:40:51Z"},{"alias_kind":"pith_short_8","alias_value":"DWP63CKB","created_at":"2026-07-05T08:40:51Z"}],"graph_snapshots":[{"event_id":"sha256:88388247aa3ef40a8403acef3ca4485d6fff506091b59844780af85ee3a06f68","target":"graph","created_at":"2026-07-05T08:40:51Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2205.15745/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The aim of Few-Shot learning methods is to train models which can easily adapt to previously unseen tasks, based on small amounts of data. One of the most popular and elegant Few-Shot learning approaches is Model-Agnostic Meta-Learning (MAML). The main idea behind this method is to learn the general weights of the meta-model, which are further adapted to specific problems in a small number of gradient steps. However, the model's main limitation lies in the fact that the update procedure is realized by gradient-based optimisation. In consequence, MAML cannot always modify weights to the essenti","authors_text":"J. Tabor, M. Przewi\\k{e}\\'zlikowski, M. Zi\\k{e}ba, P. Przybysz, P. Spurek","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-31T12:31:21Z","title":"HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.15745","kind":"arxiv","version":3},"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":"sha256:10309989a4f99512b7bbdbf1c3102837c0b94e2992738eb96ba53e0e774eb920","target":"record","created_at":"2026-07-05T08:40:51Z","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":"48ef6b344669a7d5c6cabe2186278e4fe44b1874fe6e7a85925529e37c4d4132","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-31T12:31:21Z","title_canon_sha256":"a1b75ec36a3da2324c505d00a0c638a0ec11881b2d9896ae31d995b3a3ece609"},"schema_version":"1.0","source":{"id":"2205.15745","kind":"arxiv","version":3}},"canonical_sha256":"1d9fed894195568b2f4d34f62796310f06fcbfc2033d908d3359b359e9f0d32a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1d9fed894195568b2f4d34f62796310f06fcbfc2033d908d3359b359e9f0d32a","first_computed_at":"2026-07-05T08:40:51.036973Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:40:51.036973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wu8agVJFUsL/SlZZ05CFNQXg/Ct4HeC2XInoVSbOyarz/u0TzHFHRwBRtwgVZX48QiHMKW+YjwztHB0HKb3wCg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:40:51.037444Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.15745","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:10309989a4f99512b7bbdbf1c3102837c0b94e2992738eb96ba53e0e774eb920","sha256:88388247aa3ef40a8403acef3ca4485d6fff506091b59844780af85ee3a06f68"],"state_sha256":"c62066d7a7c1f789235b17318d692bd7b015041e736ec8ad1872027835deb303"}