{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:ZKAUK6X5UBAQU3OD76JYZPI4N4","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":"091cea051e9f0d38e02f2e2533a9234d2cbcf7565a2f0e9b466036b63fcf65e6","cross_cats_sorted":["cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-07T04:11:34Z","title_canon_sha256":"61bdd3bcfbd6d941ba9ca8e04d8e9dc1a9a06f8434924ccb590e16889f19f343"},"schema_version":"1.0","source":{"id":"1910.02600","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.02600","created_at":"2026-07-05T01:53:46Z"},{"alias_kind":"arxiv_version","alias_value":"1910.02600v2","created_at":"2026-07-05T01:53:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.02600","created_at":"2026-07-05T01:53:46Z"},{"alias_kind":"pith_short_12","alias_value":"ZKAUK6X5UBAQ","created_at":"2026-07-05T01:53:46Z"},{"alias_kind":"pith_short_16","alias_value":"ZKAUK6X5UBAQU3OD","created_at":"2026-07-05T01:53:46Z"},{"alias_kind":"pith_short_8","alias_value":"ZKAUK6X5","created_at":"2026-07-05T01:53:46Z"}],"graph_snapshots":[{"event_id":"sha256:d972a70cd591009899bef4d3f2446d7a109dea9b75ec0e33577be927e6364b4c","target":"graph","created_at":"2026-07-05T01:53:46Z","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/1910.02600/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this paper, we propose a novel method for training non-Bayesian NNs to estimate a continuous target as well as its associated evidence in order to learn both aleatoric and epistemic uncertainty. We accomplish this by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparameters of the evidential distribution. We additionally impose priors during training such th","authors_text":"Alexander Amini, Ava Soleimany, Daniela Rus, Wilko Schwarting","cross_cats":["cs.NE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-07T04:11:34Z","title":"Deep Evidential Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.02600","kind":"arxiv","version":2},"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:bab2ea64a7ea9602a4bf979bbd9c2be9b387bf9f4a34ab933e3d655a66288b72","target":"record","created_at":"2026-07-05T01:53:46Z","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":"091cea051e9f0d38e02f2e2533a9234d2cbcf7565a2f0e9b466036b63fcf65e6","cross_cats_sorted":["cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-07T04:11:34Z","title_canon_sha256":"61bdd3bcfbd6d941ba9ca8e04d8e9dc1a9a06f8434924ccb590e16889f19f343"},"schema_version":"1.0","source":{"id":"1910.02600","kind":"arxiv","version":2}},"canonical_sha256":"ca81457afda0410a6dc3ff938cbd1c6f0bb44d76028213491fb6a47bb21ae605","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ca81457afda0410a6dc3ff938cbd1c6f0bb44d76028213491fb6a47bb21ae605","first_computed_at":"2026-07-05T01:53:46.587640Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:53:46.587640Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HE2EL0H7N7sBKuWfDZXFBANiImuX5k+Gs01IpcGyyEbvlZRmqOUvBM4BD07Wvj58+fQLqLIQfGS5QKoYJt6NAw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:53:46.588103Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.02600","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bab2ea64a7ea9602a4bf979bbd9c2be9b387bf9f4a34ab933e3d655a66288b72","sha256:d972a70cd591009899bef4d3f2446d7a109dea9b75ec0e33577be927e6364b4c"],"state_sha256":"26daa98dffe8f271f466475d2757b0a400e768e1d3b88e568e2ad71cdcc96c11"}