{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:R4OR3635I7E6MS3JIVYPC4AU3Y","short_pith_number":"pith:R4OR3635","schema_version":"1.0","canonical_sha256":"8f1d1dfb7d47c9e64b694570f17014de15236d7557ef7a9a621d0b2290108f5c","source":{"kind":"arxiv","id":"2307.04722","version":2},"attestation_state":"computed","paper":{"title":"Advances and Challenges in Meta-Learning: A Technical Review","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anna Vettoruzzo, Joaquin Vanschoren, KC Santosh, Mohamed-Rafik Bouguelia, Thorsteinn R\\\"ognvaldsson","submitted_at":"2023-07-10T17:32:15Z","abstract_excerpt":"Meta-learning empowers learning systems with the ability to acquire knowledge from multiple tasks, enabling faster adaptation and generalization to new tasks. This review provides a comprehensive technical overview of meta-learning, emphasizing its importance in real-world applications where data may be scarce or expensive to obtain. The paper covers the state-of-the-art meta-learning approaches and explores the relationship between meta-learning and multi-task learning, transfer learning, domain adaptation and generalization, self-supervised learning, personalized federated learning, and cont"},"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":"2307.04722","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-10T17:32:15Z","cross_cats_sorted":[],"title_canon_sha256":"1209d7ffc0e472cd920e444269971c1d01f25d321d3c6d879d189dbe44f339b5","abstract_canon_sha256":"9adc16bd6e102b6825313d46709e955aee85e2da0e665e3ea2e0991e16fcb405"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:02:11.301916Z","signature_b64":"DE09F2SJGbJiFz13eXeC1Hd8PtYfaVzxO04MxAofHGKzjrJuoecqcKe58mNQ42siHtwv9M08/TNm1b7x1H2AAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f1d1dfb7d47c9e64b694570f17014de15236d7557ef7a9a621d0b2290108f5c","last_reissued_at":"2026-06-01T01:02:11.300869Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:02:11.300869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Advances and Challenges in Meta-Learning: A Technical Review","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anna Vettoruzzo, Joaquin Vanschoren, KC Santosh, Mohamed-Rafik Bouguelia, Thorsteinn R\\\"ognvaldsson","submitted_at":"2023-07-10T17:32:15Z","abstract_excerpt":"Meta-learning empowers learning systems with the ability to acquire knowledge from multiple tasks, enabling faster adaptation and generalization to new tasks. This review provides a comprehensive technical overview of meta-learning, emphasizing its importance in real-world applications where data may be scarce or expensive to obtain. The paper covers the state-of-the-art meta-learning approaches and explores the relationship between meta-learning and multi-task learning, transfer learning, domain adaptation and generalization, self-supervised learning, personalized federated learning, and cont"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.04722","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/2307.04722/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":"2307.04722","created_at":"2026-06-01T01:02:11.301017+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.04722v2","created_at":"2026-06-01T01:02:11.301017+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.04722","created_at":"2026-06-01T01:02:11.301017+00:00"},{"alias_kind":"pith_short_12","alias_value":"R4OR3635I7E6","created_at":"2026-06-01T01:02:11.301017+00:00"},{"alias_kind":"pith_short_16","alias_value":"R4OR3635I7E6MS3J","created_at":"2026-06-01T01:02:11.301017+00:00"},{"alias_kind":"pith_short_8","alias_value":"R4OR3635","created_at":"2026-06-01T01:02:11.301017+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/R4OR3635I7E6MS3JIVYPC4AU3Y","json":"https://pith.science/pith/R4OR3635I7E6MS3JIVYPC4AU3Y.json","graph_json":"https://pith.science/api/pith-number/R4OR3635I7E6MS3JIVYPC4AU3Y/graph.json","events_json":"https://pith.science/api/pith-number/R4OR3635I7E6MS3JIVYPC4AU3Y/events.json","paper":"https://pith.science/paper/R4OR3635"},"agent_actions":{"view_html":"https://pith.science/pith/R4OR3635I7E6MS3JIVYPC4AU3Y","download_json":"https://pith.science/pith/R4OR3635I7E6MS3JIVYPC4AU3Y.json","view_paper":"https://pith.science/paper/R4OR3635","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.04722&json=true","fetch_graph":"https://pith.science/api/pith-number/R4OR3635I7E6MS3JIVYPC4AU3Y/graph.json","fetch_events":"https://pith.science/api/pith-number/R4OR3635I7E6MS3JIVYPC4AU3Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R4OR3635I7E6MS3JIVYPC4AU3Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R4OR3635I7E6MS3JIVYPC4AU3Y/action/storage_attestation","attest_author":"https://pith.science/pith/R4OR3635I7E6MS3JIVYPC4AU3Y/action/author_attestation","sign_citation":"https://pith.science/pith/R4OR3635I7E6MS3JIVYPC4AU3Y/action/citation_signature","submit_replication":"https://pith.science/pith/R4OR3635I7E6MS3JIVYPC4AU3Y/action/replication_record"}},"created_at":"2026-06-01T01:02:11.301017+00:00","updated_at":"2026-06-01T01:02:11.301017+00:00"}