{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:ETSFXEH4GRWLX43RB6BA5XUWLA","short_pith_number":"pith:ETSFXEH4","canonical_record":{"source":{"id":"1802.10501","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-28T16:06:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"24f88db651a098d04a8c8cb7f7e3d01e3a87dbd466e1eb60309523a609c46408","abstract_canon_sha256":"7e77c79a66ff65a16f8a0167881c8eacaacb452f6332ac07280f54d6cbdbe481"},"schema_version":"1.0"},"canonical_sha256":"24e45b90fc346cbbf3710f820ede96581f1867d8c361f3ca2f8785917f776259","source":{"kind":"arxiv","id":"1802.10501","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.10501","created_at":"2026-05-17T23:59:32Z"},{"alias_kind":"arxiv_version","alias_value":"1802.10501v4","created_at":"2026-05-17T23:59:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.10501","created_at":"2026-05-17T23:59:32Z"},{"alias_kind":"pith_short_12","alias_value":"ETSFXEH4GRWL","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"ETSFXEH4GRWLX43R","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"ETSFXEH4","created_at":"2026-05-18T12:32:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:ETSFXEH4GRWLX43RB6BA5XUWLA","target":"record","payload":{"canonical_record":{"source":{"id":"1802.10501","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-28T16:06:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"24f88db651a098d04a8c8cb7f7e3d01e3a87dbd466e1eb60309523a609c46408","abstract_canon_sha256":"7e77c79a66ff65a16f8a0167881c8eacaacb452f6332ac07280f54d6cbdbe481"},"schema_version":"1.0"},"canonical_sha256":"24e45b90fc346cbbf3710f820ede96581f1867d8c361f3ca2f8785917f776259","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:32.978508Z","signature_b64":"lM9aDdCcMAbyqLblrxr/5iRuHce1gNVwTcfnit/wJsAOwIw+URxz0MQN4JQjSzmCaWWVYNNPNoYrMV+3YjN/Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24e45b90fc346cbbf3710f820ede96581f1867d8c361f3ca2f8785917f776259","last_reissued_at":"2026-05-17T23:59:32.977859Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:32.977859Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.10501","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:59:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/m0GvUaYPA2BdRKQkY5fsEutvn2bLQpKsnGnnjGJLr0qHWv27KveveEmKZ0y4aKFZRICbG49dJtcQ/EMS2HODQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T17:45:18.151709Z"},"content_sha256":"3f81eec59a45386a29ef4a88b4dab599182b41f9356ddfeca5abfb484cc3dcd7","schema_version":"1.0","event_id":"sha256:3f81eec59a45386a29ef4a88b4dab599182b41f9356ddfeca5abfb484cc3dcd7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:ETSFXEH4GRWLX43RB6BA5XUWLA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Predictive Uncertainty Estimation via Prior Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Andrey Malinin, Mark Gales","submitted_at":"2018-02-28T16:06:19Z","abstract_excerpt":"Estimating how uncertain an AI system is in its predictions is important to improve the safety of such systems. Uncertainty in predictive can result from uncertainty in model parameters, irreducible data uncertainty and uncertainty due to distributional mismatch between the test and training data distributions. Different actions might be taken depending on the source of the uncertainty so it is important to be able to distinguish between them. Recently, baseline tasks and metrics have been defined and several practical methods to estimate uncertainty developed. These methods, however, attempt "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.10501","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:59:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ooh/rYvA7F/KEfyj4SjC+aCDIN+z0v2M5+LVob0cou30VnVWtJUzG3yCAwj0B+bztKxlpn0dDP4I9cus3zf0BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T17:45:18.152068Z"},"content_sha256":"19f8005e05030b4e30d7025b84461fd4114d13d044891bb82490f9e2142898cd","schema_version":"1.0","event_id":"sha256:19f8005e05030b4e30d7025b84461fd4114d13d044891bb82490f9e2142898cd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ETSFXEH4GRWLX43RB6BA5XUWLA/bundle.json","state_url":"https://pith.science/pith/ETSFXEH4GRWLX43RB6BA5XUWLA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ETSFXEH4GRWLX43RB6BA5XUWLA/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-04T17:45:18Z","links":{"resolver":"https://pith.science/pith/ETSFXEH4GRWLX43RB6BA5XUWLA","bundle":"https://pith.science/pith/ETSFXEH4GRWLX43RB6BA5XUWLA/bundle.json","state":"https://pith.science/pith/ETSFXEH4GRWLX43RB6BA5XUWLA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ETSFXEH4GRWLX43RB6BA5XUWLA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:ETSFXEH4GRWLX43RB6BA5XUWLA","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":"7e77c79a66ff65a16f8a0167881c8eacaacb452f6332ac07280f54d6cbdbe481","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-28T16:06:19Z","title_canon_sha256":"24f88db651a098d04a8c8cb7f7e3d01e3a87dbd466e1eb60309523a609c46408"},"schema_version":"1.0","source":{"id":"1802.10501","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.10501","created_at":"2026-05-17T23:59:32Z"},{"alias_kind":"arxiv_version","alias_value":"1802.10501v4","created_at":"2026-05-17T23:59:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.10501","created_at":"2026-05-17T23:59:32Z"},{"alias_kind":"pith_short_12","alias_value":"ETSFXEH4GRWL","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"ETSFXEH4GRWLX43R","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"ETSFXEH4","created_at":"2026-05-18T12:32:22Z"}],"graph_snapshots":[{"event_id":"sha256:19f8005e05030b4e30d7025b84461fd4114d13d044891bb82490f9e2142898cd","target":"graph","created_at":"2026-05-17T23:59:32Z","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"},"paper":{"abstract_excerpt":"Estimating how uncertain an AI system is in its predictions is important to improve the safety of such systems. Uncertainty in predictive can result from uncertainty in model parameters, irreducible data uncertainty and uncertainty due to distributional mismatch between the test and training data distributions. Different actions might be taken depending on the source of the uncertainty so it is important to be able to distinguish between them. Recently, baseline tasks and metrics have been defined and several practical methods to estimate uncertainty developed. These methods, however, attempt ","authors_text":"Andrey Malinin, Mark Gales","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-28T16:06:19Z","title":"Predictive Uncertainty Estimation via Prior Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.10501","kind":"arxiv","version":4},"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":"sha256:3f81eec59a45386a29ef4a88b4dab599182b41f9356ddfeca5abfb484cc3dcd7","target":"record","created_at":"2026-05-17T23:59:32Z","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":"7e77c79a66ff65a16f8a0167881c8eacaacb452f6332ac07280f54d6cbdbe481","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-28T16:06:19Z","title_canon_sha256":"24f88db651a098d04a8c8cb7f7e3d01e3a87dbd466e1eb60309523a609c46408"},"schema_version":"1.0","source":{"id":"1802.10501","kind":"arxiv","version":4}},"canonical_sha256":"24e45b90fc346cbbf3710f820ede96581f1867d8c361f3ca2f8785917f776259","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"24e45b90fc346cbbf3710f820ede96581f1867d8c361f3ca2f8785917f776259","first_computed_at":"2026-05-17T23:59:32.977859Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:32.977859Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lM9aDdCcMAbyqLblrxr/5iRuHce1gNVwTcfnit/wJsAOwIw+URxz0MQN4JQjSzmCaWWVYNNPNoYrMV+3YjN/Bw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:32.978508Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.10501","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3f81eec59a45386a29ef4a88b4dab599182b41f9356ddfeca5abfb484cc3dcd7","sha256:19f8005e05030b4e30d7025b84461fd4114d13d044891bb82490f9e2142898cd"],"state_sha256":"2a177c32f7167f6ad2999079aff305410f306b51fbe7f1f6b6fbbdea8145c6d7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U18Y6ejGPQbLF/YEJNJhLiUaHqbwHec+YRjLuu95zBsMCbdfNwJoqhV1lQv6ur84n36mGSk+YzfyIPWvoQO1Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T17:45:18.154037Z","bundle_sha256":"47023ee81ccdff62eee70c7b76f86afae83fddfffc3b78b9bfc116ed5d730ea4"}}