{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:TXQZPCMPMNQE2C5DT4GH7WHPHU","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":"f3ea33237a259ab46e8cf5eebd5350b439692f4264e08be891ea919099b5146b","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-16T17:55:55Z","title_canon_sha256":"3d5e4249e390e83b4ecf514a00ee86ae410f3e53e2bfd3a14ad0bff01ebc1ea5"},"schema_version":"1.0","source":{"id":"2412.12030","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.12030","created_at":"2026-07-05T09:49:55Z"},{"alias_kind":"arxiv_version","alias_value":"2412.12030v1","created_at":"2026-07-05T09:49:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.12030","created_at":"2026-07-05T09:49:55Z"},{"alias_kind":"pith_short_12","alias_value":"TXQZPCMPMNQE","created_at":"2026-07-05T09:49:55Z"},{"alias_kind":"pith_short_16","alias_value":"TXQZPCMPMNQE2C5D","created_at":"2026-07-05T09:49:55Z"},{"alias_kind":"pith_short_8","alias_value":"TXQZPCMP","created_at":"2026-07-05T09:49:55Z"}],"graph_snapshots":[{"event_id":"sha256:9c6929a59101226b35fafc2a340bb6d17ed123d1a292ecb8f3bbbac91de49c17","target":"graph","created_at":"2026-07-05T09:49:55Z","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/2412.12030/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The optimization-based meta-learning approach is gaining increased traction because of its unique ability to quickly adapt to a new task using only small amounts of data. However, existing optimization-based meta-learning approaches, such as MAML, ANIL and their variants, generally employ backpropagation for upper-level gradient estimation, which requires using historical lower-level parameters/gradients and thus increases computational and memory overhead in each iteration. In this paper, we propose a meta-learning algorithm that can avoid using historical parameters/gradients and significant","authors_text":"Honglin Yang, Ji Ma, Xiao Yu","cross_cats":["math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-16T17:55:55Z","title":"Memory-Reduced Meta-Learning with Guaranteed Convergence"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.12030","kind":"arxiv","version":1},"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:e061dcaab7f349c6342f1b94ed18e1eeaf7b79a4938f684eb7cc0cab81ce5aad","target":"record","created_at":"2026-07-05T09:49:55Z","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":"f3ea33237a259ab46e8cf5eebd5350b439692f4264e08be891ea919099b5146b","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-16T17:55:55Z","title_canon_sha256":"3d5e4249e390e83b4ecf514a00ee86ae410f3e53e2bfd3a14ad0bff01ebc1ea5"},"schema_version":"1.0","source":{"id":"2412.12030","kind":"arxiv","version":1}},"canonical_sha256":"9de197898f63604d0ba39f0c7fd8ef3d29027eee8f8374caaacfaf61d7380e4b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9de197898f63604d0ba39f0c7fd8ef3d29027eee8f8374caaacfaf61d7380e4b","first_computed_at":"2026-07-05T09:49:55.684540Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:49:55.684540Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AKGNedhXBBFgXkkiiC07oUpXiDSeLiq+FXT+q1Ab4RKuYHeicT9j3iSeEbWXYz3WtVlCXhZ/fQwsDj9BBUqTCg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:49:55.685195Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.12030","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e061dcaab7f349c6342f1b94ed18e1eeaf7b79a4938f684eb7cc0cab81ce5aad","sha256:9c6929a59101226b35fafc2a340bb6d17ed123d1a292ecb8f3bbbac91de49c17"],"state_sha256":"b15bc15f76b6a1629362e4b044db1b2de3a4239d8113fdb0b56841ed0ffc69be"}