{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:IMKCGFLMQXX2EFM3COSVIIUDJF","short_pith_number":"pith:IMKCGFLM","schema_version":"1.0","canonical_sha256":"431423156c85efa2159b13a554228349788c7f0333637e264bf5befe3d687542","source":{"kind":"arxiv","id":"2207.03804","version":2},"attestation_state":"computed","paper":{"title":"On the Subspace Structure of Gradient-Based Meta-Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alfredo Reichlin, Danica Kragic, Gustaf Tegn\\'er, Hang Yin, M{\\aa}rten Bj\\\"orkman","submitted_at":"2022-07-08T10:19:15Z","abstract_excerpt":"In this work we provide an analysis of the distribution of the post-adaptation parameters of Gradient-Based Meta-Learning (GBML) methods. Previous work has noticed how, for the case of image-classification, this adaptation only takes place on the last layers of the network. We propose the more general notion that parameters are updated over a low-dimensional \\emph{subspace} of the same dimensionality as the task-space and show that this holds for regression as well. Furthermore, the induced subspace structure provides a method to estimate the intrinsic dimension of the space of tasks of common"},"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":"2207.03804","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-08T10:19:15Z","cross_cats_sorted":[],"title_canon_sha256":"824ccda3a743c8497076903d0e007bb24dd999a572b8ead03bd8f423488b84ba","abstract_canon_sha256":"718c1c53c39c377288f81d19d68e08a08b0fa85f70b8e1d0808d6380e2120f6c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:02:12.568161Z","signature_b64":"Lri5EhXh0ktnyEWQQzXQRpPCokbPlMKO0tb7E+OPuGpZihJGBsDngeeLoW4glqHE3DDn65tQkOA9a7osTAMeCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"431423156c85efa2159b13a554228349788c7f0333637e264bf5befe3d687542","last_reissued_at":"2026-07-05T05:02:12.567617Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:02:12.567617Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Subspace Structure of Gradient-Based Meta-Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alfredo Reichlin, Danica Kragic, Gustaf Tegn\\'er, Hang Yin, M{\\aa}rten Bj\\\"orkman","submitted_at":"2022-07-08T10:19:15Z","abstract_excerpt":"In this work we provide an analysis of the distribution of the post-adaptation parameters of Gradient-Based Meta-Learning (GBML) methods. Previous work has noticed how, for the case of image-classification, this adaptation only takes place on the last layers of the network. We propose the more general notion that parameters are updated over a low-dimensional \\emph{subspace} of the same dimensionality as the task-space and show that this holds for regression as well. Furthermore, the induced subspace structure provides a method to estimate the intrinsic dimension of the space of tasks of common"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.03804","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/2207.03804/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":"2207.03804","created_at":"2026-07-05T05:02:12.567680+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.03804v2","created_at":"2026-07-05T05:02:12.567680+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.03804","created_at":"2026-07-05T05:02:12.567680+00:00"},{"alias_kind":"pith_short_12","alias_value":"IMKCGFLMQXX2","created_at":"2026-07-05T05:02:12.567680+00:00"},{"alias_kind":"pith_short_16","alias_value":"IMKCGFLMQXX2EFM3","created_at":"2026-07-05T05:02:12.567680+00:00"},{"alias_kind":"pith_short_8","alias_value":"IMKCGFLM","created_at":"2026-07-05T05:02:12.567680+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/IMKCGFLMQXX2EFM3COSVIIUDJF","json":"https://pith.science/pith/IMKCGFLMQXX2EFM3COSVIIUDJF.json","graph_json":"https://pith.science/api/pith-number/IMKCGFLMQXX2EFM3COSVIIUDJF/graph.json","events_json":"https://pith.science/api/pith-number/IMKCGFLMQXX2EFM3COSVIIUDJF/events.json","paper":"https://pith.science/paper/IMKCGFLM"},"agent_actions":{"view_html":"https://pith.science/pith/IMKCGFLMQXX2EFM3COSVIIUDJF","download_json":"https://pith.science/pith/IMKCGFLMQXX2EFM3COSVIIUDJF.json","view_paper":"https://pith.science/paper/IMKCGFLM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.03804&json=true","fetch_graph":"https://pith.science/api/pith-number/IMKCGFLMQXX2EFM3COSVIIUDJF/graph.json","fetch_events":"https://pith.science/api/pith-number/IMKCGFLMQXX2EFM3COSVIIUDJF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IMKCGFLMQXX2EFM3COSVIIUDJF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IMKCGFLMQXX2EFM3COSVIIUDJF/action/storage_attestation","attest_author":"https://pith.science/pith/IMKCGFLMQXX2EFM3COSVIIUDJF/action/author_attestation","sign_citation":"https://pith.science/pith/IMKCGFLMQXX2EFM3COSVIIUDJF/action/citation_signature","submit_replication":"https://pith.science/pith/IMKCGFLMQXX2EFM3COSVIIUDJF/action/replication_record"}},"created_at":"2026-07-05T05:02:12.567680+00:00","updated_at":"2026-07-05T05:02:12.567680+00:00"}