{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:TNJ5W3L2GTX554D5WD5DTYFHAK","short_pith_number":"pith:TNJ5W3L2","canonical_record":{"source":{"id":"1912.05651","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-12-11T21:37:54Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"25b11c9f4d22df6fdd13bf3257b4b16507f2eed80bd9bf2468ceb5e437a6ae8d","abstract_canon_sha256":"9d45fb74de4cd3b6885a5ba8ddecfd9cf49fb37d0ed7cfaaf891e6719ada5214"},"schema_version":"1.0"},"canonical_sha256":"9b53db6d7a34efdef07db0fa39e0a70295e81084c246fae7f634ee9b78df5437","source":{"kind":"arxiv","id":"1912.05651","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.05651","created_at":"2026-07-05T01:19:25Z"},{"alias_kind":"arxiv_version","alias_value":"1912.05651v3","created_at":"2026-07-05T01:19:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.05651","created_at":"2026-07-05T01:19:25Z"},{"alias_kind":"pith_short_12","alias_value":"TNJ5W3L2GTX5","created_at":"2026-07-05T01:19:25Z"},{"alias_kind":"pith_short_16","alias_value":"TNJ5W3L2GTX554D5","created_at":"2026-07-05T01:19:25Z"},{"alias_kind":"pith_short_8","alias_value":"TNJ5W3L2","created_at":"2026-07-05T01:19:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:TNJ5W3L2GTX554D5WD5DTYFHAK","target":"record","payload":{"canonical_record":{"source":{"id":"1912.05651","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-12-11T21:37:54Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"25b11c9f4d22df6fdd13bf3257b4b16507f2eed80bd9bf2468ceb5e437a6ae8d","abstract_canon_sha256":"9d45fb74de4cd3b6885a5ba8ddecfd9cf49fb37d0ed7cfaaf891e6719ada5214"},"schema_version":"1.0"},"canonical_sha256":"9b53db6d7a34efdef07db0fa39e0a70295e81084c246fae7f634ee9b78df5437","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:19:25.642481Z","signature_b64":"zZzQPJ1SMWmaAciEUVWIaR/Pjo15N+eup+IxHqpaI8zhkq/PW3ixFCfzTQpXlFm0AVWXT0DHwlD1y2TLLFOiBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b53db6d7a34efdef07db0fa39e0a70295e81084c246fae7f634ee9b78df5437","last_reissued_at":"2026-07-05T01:19:25.641979Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:19:25.641979Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1912.05651","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:19:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eP8alq1KmkofXs0bWjJyjulkQdsXoJa5UlhQN3xqMDhjMyhtIs2eAKPFnETdVGlT4jiqNyNHoWcv7h05roLUCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T01:22:55.979144Z"},"content_sha256":"f28eac7a4a323df9eba9f63a5fe9124a11ea4bdf8dffd9585ae50f45ca9cc31b","schema_version":"1.0","event_id":"sha256:f28eac7a4a323df9eba9f63a5fe9124a11ea4bdf8dffd9585ae50f45ca9cc31b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:TNJ5W3L2GTX554D5WD5DTYFHAK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Erik Daxberger, Jos\\'e Miguel Hern\\'andez-Lobato","submitted_at":"2019-12-11T21:37:54Z","abstract_excerpt":"Despite their successes, deep neural networks may make unreliable predictions when faced with test data drawn from a distribution different to that of the training data, constituting a major problem for AI safety. While this has recently motivated the development of methods to detect such out-of-distribution (OoD) inputs, a robust solution is still lacking. We propose a new probabilistic, unsupervised approach to this problem based on a Bayesian variational autoencoder model, which estimates a full posterior distribution over the decoder parameters using stochastic gradient Markov chain Monte "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.05651","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1912.05651/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T01:19:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"km9btv41Rl75I7UDmrYQp+TjFAMet6nLJnkYonf8uRAZX/XibBvPa/YLHhhvDrYpwIKedhmRpbXMAYDD94+EDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T01:22:55.979648Z"},"content_sha256":"a14e1df2c2e103303bd22cb9fc9c34cfd2f3272ce79e9cb5e73dcbcce2ca5833","schema_version":"1.0","event_id":"sha256:a14e1df2c2e103303bd22cb9fc9c34cfd2f3272ce79e9cb5e73dcbcce2ca5833"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TNJ5W3L2GTX554D5WD5DTYFHAK/bundle.json","state_url":"https://pith.science/pith/TNJ5W3L2GTX554D5WD5DTYFHAK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TNJ5W3L2GTX554D5WD5DTYFHAK/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":"2026-08-20T01:22:55Z","links":{"resolver":"https://pith.science/pith/TNJ5W3L2GTX554D5WD5DTYFHAK","bundle":"https://pith.science/pith/TNJ5W3L2GTX554D5WD5DTYFHAK/bundle.json","state":"https://pith.science/pith/TNJ5W3L2GTX554D5WD5DTYFHAK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TNJ5W3L2GTX554D5WD5DTYFHAK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:TNJ5W3L2GTX554D5WD5DTYFHAK","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":"9d45fb74de4cd3b6885a5ba8ddecfd9cf49fb37d0ed7cfaaf891e6719ada5214","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-12-11T21:37:54Z","title_canon_sha256":"25b11c9f4d22df6fdd13bf3257b4b16507f2eed80bd9bf2468ceb5e437a6ae8d"},"schema_version":"1.0","source":{"id":"1912.05651","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.05651","created_at":"2026-07-05T01:19:25Z"},{"alias_kind":"arxiv_version","alias_value":"1912.05651v3","created_at":"2026-07-05T01:19:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.05651","created_at":"2026-07-05T01:19:25Z"},{"alias_kind":"pith_short_12","alias_value":"TNJ5W3L2GTX5","created_at":"2026-07-05T01:19:25Z"},{"alias_kind":"pith_short_16","alias_value":"TNJ5W3L2GTX554D5","created_at":"2026-07-05T01:19:25Z"},{"alias_kind":"pith_short_8","alias_value":"TNJ5W3L2","created_at":"2026-07-05T01:19:25Z"}],"graph_snapshots":[{"event_id":"sha256:a14e1df2c2e103303bd22cb9fc9c34cfd2f3272ce79e9cb5e73dcbcce2ca5833","target":"graph","created_at":"2026-07-05T01:19:25Z","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/1912.05651/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite their successes, deep neural networks may make unreliable predictions when faced with test data drawn from a distribution different to that of the training data, constituting a major problem for AI safety. While this has recently motivated the development of methods to detect such out-of-distribution (OoD) inputs, a robust solution is still lacking. We propose a new probabilistic, unsupervised approach to this problem based on a Bayesian variational autoencoder model, which estimates a full posterior distribution over the decoder parameters using stochastic gradient Markov chain Monte ","authors_text":"Erik Daxberger, Jos\\'e Miguel Hern\\'andez-Lobato","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-12-11T21:37:54Z","title":"Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.05651","kind":"arxiv","version":3},"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:f28eac7a4a323df9eba9f63a5fe9124a11ea4bdf8dffd9585ae50f45ca9cc31b","target":"record","created_at":"2026-07-05T01:19:25Z","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":"9d45fb74de4cd3b6885a5ba8ddecfd9cf49fb37d0ed7cfaaf891e6719ada5214","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-12-11T21:37:54Z","title_canon_sha256":"25b11c9f4d22df6fdd13bf3257b4b16507f2eed80bd9bf2468ceb5e437a6ae8d"},"schema_version":"1.0","source":{"id":"1912.05651","kind":"arxiv","version":3}},"canonical_sha256":"9b53db6d7a34efdef07db0fa39e0a70295e81084c246fae7f634ee9b78df5437","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9b53db6d7a34efdef07db0fa39e0a70295e81084c246fae7f634ee9b78df5437","first_computed_at":"2026-07-05T01:19:25.641979Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:19:25.641979Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zZzQPJ1SMWmaAciEUVWIaR/Pjo15N+eup+IxHqpaI8zhkq/PW3ixFCfzTQpXlFm0AVWXT0DHwlD1y2TLLFOiBg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:19:25.642481Z","signed_message":"canonical_sha256_bytes"},"source_id":"1912.05651","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f28eac7a4a323df9eba9f63a5fe9124a11ea4bdf8dffd9585ae50f45ca9cc31b","sha256:a14e1df2c2e103303bd22cb9fc9c34cfd2f3272ce79e9cb5e73dcbcce2ca5833"],"state_sha256":"b91872a67f199c3897d3da60ef462025c398d3cd43534270149a0e3e16ca88fb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"blEyrAcbZSfIsaEIA+uKE38WchlOrQz5iOYZDvQShi/+NrVXUwf+CVKBjO0k+1JZDwlr3qur2MgQhWRJN/C5Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T01:22:55.984959Z","bundle_sha256":"204f56eacfbafe8d160cd6417026081f0ad973c3bc22f095b4f3522d80ca904f"}}