{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:DSQQEHAZSBEZLMRHUL7IBVXKXV","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":"be98082940d6f969bb544f3d20a01226ea254f07305115a30a1d1d673ae9cd84","cross_cats_sorted":["cs.DC","cs.PF"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-10-27T14:27:36Z","title_canon_sha256":"80c37728860f903fa63dd4f72adb753749e2fc87a6b8b6de23dd0156b8ab2570"},"schema_version":"1.0","source":{"id":"2110.14459","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.14459","created_at":"2026-07-05T03:26:28Z"},{"alias_kind":"arxiv_version","alias_value":"2110.14459v1","created_at":"2026-07-05T03:26:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.14459","created_at":"2026-07-05T03:26:28Z"},{"alias_kind":"pith_short_12","alias_value":"DSQQEHAZSBEZ","created_at":"2026-07-05T03:26:28Z"},{"alias_kind":"pith_short_16","alias_value":"DSQQEHAZSBEZLMRH","created_at":"2026-07-05T03:26:28Z"},{"alias_kind":"pith_short_8","alias_value":"DSQQEHAZ","created_at":"2026-07-05T03:26:28Z"}],"graph_snapshots":[{"event_id":"sha256:4844ad3a7edd50910e266fe7fc8c444b5d43d6e6f428e5c14c82793cd5bc06c1","target":"graph","created_at":"2026-07-05T03:26:28Z","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/2110.14459/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Meta Learning has been in focus in recent years due to the meta-learner model's ability to adapt well and generalize to new tasks, thus, reducing both the time and data requirements for learning. However, a major drawback of meta learner is that, to reach to a state from where learning new tasks becomes feasible with less data, it requires a large number of iterations and a lot of time. We address this issue by proposing various acceleration techniques to speed up meta learning algorithms such as MAML (Model Agnostic Meta Learning). We present 3.73X acceleration on a well known RNN optimizer b","authors_text":"Amey Pandit, Mayank Mishra, Rekha Singhal, Varad Pimpalkhute","cross_cats":["cs.DC","cs.PF"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-10-27T14:27:36Z","title":"Accelerating Gradient-based Meta Learner"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.14459","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:c03e19233081b1fa58c71fb4568dd811cd6607b962a8fba8cb98596f55324b78","target":"record","created_at":"2026-07-05T03:26:28Z","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":"be98082940d6f969bb544f3d20a01226ea254f07305115a30a1d1d673ae9cd84","cross_cats_sorted":["cs.DC","cs.PF"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-10-27T14:27:36Z","title_canon_sha256":"80c37728860f903fa63dd4f72adb753749e2fc87a6b8b6de23dd0156b8ab2570"},"schema_version":"1.0","source":{"id":"2110.14459","kind":"arxiv","version":1}},"canonical_sha256":"1ca1021c19904995b227a2fe80d6eabd75bb3c091cefad45b7eb4f9aa0260ca3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1ca1021c19904995b227a2fe80d6eabd75bb3c091cefad45b7eb4f9aa0260ca3","first_computed_at":"2026-07-05T03:26:28.089072Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:26:28.089072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"G+SIdlDA8XBzTWjPP1fHXNqdZmgBA95gBXb/+cZBeOu9Vx7uQGxvVX4qcbubb/00oNXpYt8Hh7aWzLwPX/RYDw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:26:28.089610Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.14459","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c03e19233081b1fa58c71fb4568dd811cd6607b962a8fba8cb98596f55324b78","sha256:4844ad3a7edd50910e266fe7fc8c444b5d43d6e6f428e5c14c82793cd5bc06c1"],"state_sha256":"99c623bf0e6e6a85037cf43c667c087ed9931d114355becc7ec6b5dddc75b8f6"}