{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:5RWJKKVYMVCJJIH5G4H575EVF7","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":"78f234d8cfd1a70bb0b2f8275c7c6c037b1b81eb8e86cd88525be9a539668a7d","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-05-07T14:16:03Z","title_canon_sha256":"b370efbe5adf5aadf85c222fe71c1b2c81e555735fe1aa7a4bcb36e9651b3eca"},"schema_version":"1.0","source":{"id":"2105.04404","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.04404","created_at":"2026-07-05T02:38:56Z"},{"alias_kind":"arxiv_version","alias_value":"2105.04404v1","created_at":"2026-07-05T02:38:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.04404","created_at":"2026-07-05T02:38:56Z"},{"alias_kind":"pith_short_12","alias_value":"5RWJKKVYMVCJ","created_at":"2026-07-05T02:38:56Z"},{"alias_kind":"pith_short_16","alias_value":"5RWJKKVYMVCJJIH5","created_at":"2026-07-05T02:38:56Z"},{"alias_kind":"pith_short_8","alias_value":"5RWJKKVY","created_at":"2026-07-05T02:38:56Z"}],"graph_snapshots":[{"event_id":"sha256:f4f90b891bf2546d17317cfac062bd1a0967515acfe19fdeeb3a961ff40fd2fe","target":"graph","created_at":"2026-07-05T02:38:56Z","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/2105.04404/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Although neural networks are capable of reaching astonishing performances on a wide variety of contexts, properly training networks on complicated tasks requires expertise and can be expensive from a computational perspective. In industrial applications, data coming from an open-world setting might widely differ from the benchmark datasets on which a network was trained. Being able to monitor the presence of such variations without retraining the network is of crucial importance. In this article, we develop a method to monitor trained neural networks based on the topological properties of thei","authors_text":"Fr\\'ed\\'eric Chazal, Marc Glisse, Mathieu Carriere, Th\\'eo Lacombe (DATASHAPE), Yuhei Umeda, Yuichi Ike","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-05-07T14:16:03Z","title":"Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.04404","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":"sha256:3d3ae99d04b895dc151967ef6345be4c9d59a6a59bbeadff58504d5995e24041","target":"record","created_at":"2026-07-05T02:38:56Z","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":"78f234d8cfd1a70bb0b2f8275c7c6c037b1b81eb8e86cd88525be9a539668a7d","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-05-07T14:16:03Z","title_canon_sha256":"b370efbe5adf5aadf85c222fe71c1b2c81e555735fe1aa7a4bcb36e9651b3eca"},"schema_version":"1.0","source":{"id":"2105.04404","kind":"arxiv","version":1}},"canonical_sha256":"ec6c952ab8654494a0fd370fdff4952fe720d40db49a558bd13fdd63440ee180","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ec6c952ab8654494a0fd370fdff4952fe720d40db49a558bd13fdd63440ee180","first_computed_at":"2026-07-05T02:38:56.881157Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:38:56.881157Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4G5eBERn/vg88V9npJmkhlalyMOx714hx/02M9GLFGdzS56EEyCuFvngPEwH4UCgGiAyoiFXjUs5pCHUjMIDAw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:38:56.881611Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.04404","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d3ae99d04b895dc151967ef6345be4c9d59a6a59bbeadff58504d5995e24041","sha256:f4f90b891bf2546d17317cfac062bd1a0967515acfe19fdeeb3a961ff40fd2fe"],"state_sha256":"be28b012756dd42211908f0e4bb70e84e7e61bd34a2666e9d4d7ca51e999aef9"}