{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:ZIROPHJDST3DECWXDFQ32XT2VJ","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":"34628c9948286a1def48155a3bfe9cd7d2fbb80600f964dbd74a0c1b755090c7","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-05T13:24:43Z","title_canon_sha256":"dcbb3fcfd6dc2ac136c0c13a727bee919c24f5a83e9863183bd3ba1ce4bbccde"},"schema_version":"1.0","source":{"id":"2010.01992","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.01992","created_at":"2026-07-05T07:05:03Z"},{"alias_kind":"arxiv_version","alias_value":"2010.01992v3","created_at":"2026-07-05T07:05:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.01992","created_at":"2026-07-05T07:05:03Z"},{"alias_kind":"pith_short_12","alias_value":"ZIROPHJDST3D","created_at":"2026-07-05T07:05:03Z"},{"alias_kind":"pith_short_16","alias_value":"ZIROPHJDST3DECWX","created_at":"2026-07-05T07:05:03Z"},{"alias_kind":"pith_short_8","alias_value":"ZIROPHJD","created_at":"2026-07-05T07:05:03Z"}],"graph_snapshots":[{"event_id":"sha256:d0ac35d77eaa013dd51c49eb24a2750ca18664eb4f111d6c019348c40f64a626","target":"graph","created_at":"2026-07-05T07:05:03Z","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/2010.01992/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we consider the framework of multi-task representation (MTR) learning where the goal is to use source tasks to learn a representation that reduces the sample complexity of solving a target task. We start by reviewing recent advances in MTR theory and show that they can provide novel insights for popular meta-learning algorithms when analyzed within this framework. In particular, we highlight a fundamental difference between gradient-based and metric-based algorithms in practice and put forward a theoretical analysis to explain it. Finally, we use the derived insights to improve ","authors_text":"Amaury Habrard, Ang\\'elique Loesch, Ievgen Redko, Quentin Bouniot, Romaric Audigier","cross_cats":["cs.AI","cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-05T13:24:43Z","title":"Improving Few-Shot Learning through Multi-task Representation Learning Theory"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.01992","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":"sha256:74c700b945212281793799a11395eea71512f3dce3bfbff82d8d54f6da02d700","target":"record","created_at":"2026-07-05T07:05:03Z","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":"34628c9948286a1def48155a3bfe9cd7d2fbb80600f964dbd74a0c1b755090c7","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-05T13:24:43Z","title_canon_sha256":"dcbb3fcfd6dc2ac136c0c13a727bee919c24f5a83e9863183bd3ba1ce4bbccde"},"schema_version":"1.0","source":{"id":"2010.01992","kind":"arxiv","version":3}},"canonical_sha256":"ca22e79d2394f6320ad71961bd5e7aaa4c72043afe3dd2b2ea927ced31146b40","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ca22e79d2394f6320ad71961bd5e7aaa4c72043afe3dd2b2ea927ced31146b40","first_computed_at":"2026-07-05T07:05:03.555708Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:05:03.555708Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"h7zUPb/V2TDbXuAwlms9L1DCiCvU+6LQhBDoq+15xv8lbRdKeY5X0MjWEj+Bft2sQc+RC/+1VUAhOrXut3nZAw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:05:03.556271Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.01992","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:74c700b945212281793799a11395eea71512f3dce3bfbff82d8d54f6da02d700","sha256:d0ac35d77eaa013dd51c49eb24a2750ca18664eb4f111d6c019348c40f64a626"],"state_sha256":"e8254c3f7c04e9f58bc0ddde79bed73cd42d830c3e7eba78a3a55314afbd038f"}