{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:GNEJ2SOK6AXL4BZ36HUA2C5R55","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":"6d6bd3de0189dbc6bd2c20885199b98807a2a767f4550285ada8b1f7cef37ba5","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-14T10:58:53Z","title_canon_sha256":"fea42199476d56ebfdc3bf68c9aea3a02f1f7cfd9cb4fa937f01666ee2c73d16"},"schema_version":"1.0","source":{"id":"1909.06576","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.06576","created_at":"2026-07-05T00:04:47Z"},{"alias_kind":"arxiv_version","alias_value":"1909.06576v1","created_at":"2026-07-05T00:04:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.06576","created_at":"2026-07-05T00:04:47Z"},{"alias_kind":"pith_short_12","alias_value":"GNEJ2SOK6AXL","created_at":"2026-07-05T00:04:47Z"},{"alias_kind":"pith_short_16","alias_value":"GNEJ2SOK6AXL4BZ3","created_at":"2026-07-05T00:04:47Z"},{"alias_kind":"pith_short_8","alias_value":"GNEJ2SOK","created_at":"2026-07-05T00:04:47Z"}],"graph_snapshots":[{"event_id":"sha256:c3afdbcd73e7f3cdda4220581958e042dd005e034452d728f036fcaf2dbeddcf","target":"graph","created_at":"2026-07-05T00:04:47Z","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/1909.06576/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research. They offer a way to get a fair comparison between different algorithms, and the wide range of datasets available allows full control over the complexity of this evaluation. However, for a large majority of code available online, the data pipeline is often specific to one dataset, and testing on another dataset requires significant rework. We introduce Torchmeta, a library built on top of PyTorch that enables seamless and consistent evaluation of meta-lea","authors_text":"Joseph Paul Cohen, Mandana Samiei, Tobias W\\\"urfl, Tristan Deleu, Yoshua Bengio","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-14T10:58:53Z","title":"Torchmeta: A Meta-Learning library for PyTorch"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.06576","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:ab6c83fd55e1913cb98d22e6d30a772bcc639155f47864a397dd12da013e21bb","target":"record","created_at":"2026-07-05T00:04:47Z","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":"6d6bd3de0189dbc6bd2c20885199b98807a2a767f4550285ada8b1f7cef37ba5","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-14T10:58:53Z","title_canon_sha256":"fea42199476d56ebfdc3bf68c9aea3a02f1f7cfd9cb4fa937f01666ee2c73d16"},"schema_version":"1.0","source":{"id":"1909.06576","kind":"arxiv","version":1}},"canonical_sha256":"33489d49caf02ebe073bf1e80d0bb1ef4b0e1caa173d6850ea672863f09419cc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"33489d49caf02ebe073bf1e80d0bb1ef4b0e1caa173d6850ea672863f09419cc","first_computed_at":"2026-07-05T00:04:47.574631Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:04:47.574631Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bQMRApFJH84iLmcMSwxopBP7dcsSjIt9ei2DSjSQFHwqfdIo1Vze7n1aSMWoLn8DSLptrmZdGbiy8jo6rbJvBA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:04:47.574964Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.06576","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ab6c83fd55e1913cb98d22e6d30a772bcc639155f47864a397dd12da013e21bb","sha256:c3afdbcd73e7f3cdda4220581958e042dd005e034452d728f036fcaf2dbeddcf"],"state_sha256":"a7bc9b9ce2628547aeb2a4beb3617ca6c1567e79d005e96a2cb49eabcf8f6da7"}