{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:CPNL2U6ITLVXGINZRQIEUHPR5A","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":"426b2b99d650dc33ca95f0cf89a16ce714c80f2d0dce75b0688cc52a447c1bda","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-25T21:42:08Z","title_canon_sha256":"50f55bc6d670ad2a6c69239de3551a7fa1189ec547edf6e794a17cb9cb8bc8a4"},"schema_version":"1.0","source":{"id":"2207.12545","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.12545","created_at":"2026-07-05T04:43:23Z"},{"alias_kind":"arxiv_version","alias_value":"2207.12545v1","created_at":"2026-07-05T04:43:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.12545","created_at":"2026-07-05T04:43:23Z"},{"alias_kind":"pith_short_12","alias_value":"CPNL2U6ITLVX","created_at":"2026-07-05T04:43:23Z"},{"alias_kind":"pith_short_16","alias_value":"CPNL2U6ITLVXGINZ","created_at":"2026-07-05T04:43:23Z"},{"alias_kind":"pith_short_8","alias_value":"CPNL2U6I","created_at":"2026-07-05T04:43:23Z"}],"graph_snapshots":[{"event_id":"sha256:626caf43a8bb615d9946468ac0cf389cbe6654da8c421ea971904df3c87102a0","target":"graph","created_at":"2026-07-05T04:43:23Z","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/2207.12545/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The lack of well-calibrated confidence estimates makes neural networks inadequate in safety-critical domains such as autonomous driving or healthcare. In these settings, having the ability to abstain from making a prediction on out-of-distribution (OOD) data can be as important as correctly classifying in-distribution data. We introduce $p$-DkNN, a novel inference procedure that takes a trained deep neural network and analyzes the similarity structures of its intermediate hidden representations to compute $p$-values associated with the end-to-end model prediction. The intuition is that statist","authors_text":"Adam Dziedzic, Armin Ale, Mohammad Yaghini, Murat A. Erdogdu, Nicolas Papernot, Stephan Rabanser","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-25T21:42:08Z","title":"$p$-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.12545","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:b9963c86d25f24c01da930b37d10e93f8a9467013c2f6125d192d0071c76e326","target":"record","created_at":"2026-07-05T04:43:23Z","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":"426b2b99d650dc33ca95f0cf89a16ce714c80f2d0dce75b0688cc52a447c1bda","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-25T21:42:08Z","title_canon_sha256":"50f55bc6d670ad2a6c69239de3551a7fa1189ec547edf6e794a17cb9cb8bc8a4"},"schema_version":"1.0","source":{"id":"2207.12545","kind":"arxiv","version":1}},"canonical_sha256":"13dabd53c89aeb7321b98c104a1df1e8321837ada41e12ba8ec1cb1e3d4830cd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"13dabd53c89aeb7321b98c104a1df1e8321837ada41e12ba8ec1cb1e3d4830cd","first_computed_at":"2026-07-05T04:43:23.730970Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:43:23.730970Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NXvND0ouWLzguHyWu9jPntQeAapauQh15C5NEtwRFckxjXZrCh+vWIad0JeM7K7f0OpvRXYUpDP7xC2ithcrCg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:43:23.731396Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.12545","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9963c86d25f24c01da930b37d10e93f8a9467013c2f6125d192d0071c76e326","sha256:626caf43a8bb615d9946468ac0cf389cbe6654da8c421ea971904df3c87102a0"],"state_sha256":"ef68179f7e9164cbd4032d764a5db5ae168e4365f3817bf6f909ba9643638380"}