{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:LYVEKVBVMW4JS6D2AOKQ7ZQ5EQ","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":"494950a69d1cb80ecf48a814870b07c4a63cb356fb4f0ae92ff7de7551563763","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-04T14:12:31Z","title_canon_sha256":"b0f138c4e5b059d62450f759da41291400e6596791e4b7a2aac54a06cb01d792"},"schema_version":"1.0","source":{"id":"2202.02142","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.02142","created_at":"2026-07-05T05:09:53Z"},{"alias_kind":"arxiv_version","alias_value":"2202.02142v6","created_at":"2026-07-05T05:09:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.02142","created_at":"2026-07-05T05:09:53Z"},{"alias_kind":"pith_short_12","alias_value":"LYVEKVBVMW4J","created_at":"2026-07-05T05:09:53Z"},{"alias_kind":"pith_short_16","alias_value":"LYVEKVBVMW4JS6D2","created_at":"2026-07-05T05:09:53Z"},{"alias_kind":"pith_short_8","alias_value":"LYVEKVBV","created_at":"2026-07-05T05:09:53Z"}],"graph_snapshots":[{"event_id":"sha256:a8c0d0fb9a695ce24b1aa2c45b9fa9a18579ce41fffa3f6d2f068c114967820d","target":"graph","created_at":"2026-07-05T05:09:53Z","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/2202.02142/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Designing learning systems which are invariant to certain data transformations is critical in machine learning. Practitioners can typically enforce a desired invariance on the trained model through the choice of a network architecture, e.g. using convolutions for translations, or using data augmentation. Yet, enforcing true invariance in the network can be difficult, and data invariances are not always known a piori. State-of-the-art methods for learning data augmentation policies require held-out data and are based on bilevel optimization problems, which are complex to solve and often computa","authors_text":"Alexandre Gramfort, C\\'edric Rommel, Thomas Moreau","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-04T14:12:31Z","title":"Deep invariant networks with differentiable augmentation layers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.02142","kind":"arxiv","version":6},"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:eeb3ed122864b24f1c876809ecbb0f7a6bdaea3e20de5ab978e170d224a80b9c","target":"record","created_at":"2026-07-05T05:09:53Z","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":"494950a69d1cb80ecf48a814870b07c4a63cb356fb4f0ae92ff7de7551563763","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-04T14:12:31Z","title_canon_sha256":"b0f138c4e5b059d62450f759da41291400e6596791e4b7a2aac54a06cb01d792"},"schema_version":"1.0","source":{"id":"2202.02142","kind":"arxiv","version":6}},"canonical_sha256":"5e2a45543565b899787a03950fe61d240d775a8030d3b47a50f1f9cf5660fa9b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5e2a45543565b899787a03950fe61d240d775a8030d3b47a50f1f9cf5660fa9b","first_computed_at":"2026-07-05T05:09:53.191707Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:09:53.191707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fateLzX0YM+rf6wDHIXk1vScOS1e6wdsVk7qbwrOTUOP7AZb4KwN3ZrSh9ba5AEhdqiKi7UjWcnVh6ZJ9U/cDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:09:53.192132Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.02142","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eeb3ed122864b24f1c876809ecbb0f7a6bdaea3e20de5ab978e170d224a80b9c","sha256:a8c0d0fb9a695ce24b1aa2c45b9fa9a18579ce41fffa3f6d2f068c114967820d"],"state_sha256":"767a3b92245cef110cd19fba0eff294e06245054e9c9982d58002a7d06089501"}