{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:XAVSZ7HVEIW3EKI73SX6RRVODB","short_pith_number":"pith:XAVSZ7HV","canonical_record":{"source":{"id":"2502.01920","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-04T01:29:22Z","cross_cats_sorted":[],"title_canon_sha256":"9afacf53c1a89fd97927d934faabca8f1aa76206b5f6e198c50abff7ca99cb52","abstract_canon_sha256":"b86c6e69d53edca40cc0e4b83cbe251a7f8f2c59beba80d8b68a911b0e26f5f1"},"schema_version":"1.0"},"canonical_sha256":"b82b2cfcf5222db2291fdcafe8c6ae187f3cf161109e6eaf3c1bb59aa27e5426","source":{"kind":"arxiv","id":"2502.01920","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.01920","created_at":"2026-07-05T11:19:51Z"},{"alias_kind":"arxiv_version","alias_value":"2502.01920v2","created_at":"2026-07-05T11:19:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01920","created_at":"2026-07-05T11:19:51Z"},{"alias_kind":"pith_short_12","alias_value":"XAVSZ7HVEIW3","created_at":"2026-07-05T11:19:51Z"},{"alias_kind":"pith_short_16","alias_value":"XAVSZ7HVEIW3EKI7","created_at":"2026-07-05T11:19:51Z"},{"alias_kind":"pith_short_8","alias_value":"XAVSZ7HV","created_at":"2026-07-05T11:19:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:XAVSZ7HVEIW3EKI73SX6RRVODB","target":"record","payload":{"canonical_record":{"source":{"id":"2502.01920","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-04T01:29:22Z","cross_cats_sorted":[],"title_canon_sha256":"9afacf53c1a89fd97927d934faabca8f1aa76206b5f6e198c50abff7ca99cb52","abstract_canon_sha256":"b86c6e69d53edca40cc0e4b83cbe251a7f8f2c59beba80d8b68a911b0e26f5f1"},"schema_version":"1.0"},"canonical_sha256":"b82b2cfcf5222db2291fdcafe8c6ae187f3cf161109e6eaf3c1bb59aa27e5426","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:19:51.109433Z","signature_b64":"leMAOUGL17jlVrix28EaR5togHovgcLEN5dr8GnU6RG+HF5dCgat9sEcxVuTAbBgRwvzuCst3Ms+/rsn5mOJCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b82b2cfcf5222db2291fdcafe8c6ae187f3cf161109e6eaf3c1bb59aa27e5426","last_reissued_at":"2026-07-05T11:19:51.109020Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:19:51.109020Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.01920","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:19:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"10qJgH//RBsDpEZ4/j//F+c/BuC/dvYP7XoYFC700UbrlG94YurdxqPgk2sLEPyrW0HN6Yy0yfzZs0rC03laDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T09:08:00.286408Z"},"content_sha256":"3509ef0fde1c4c92adc70bc001cd09158ac3abdea3070acd88f691dcf8e2ce3c","schema_version":"1.0","event_id":"sha256:3509ef0fde1c4c92adc70bc001cd09158ac3abdea3070acd88f691dcf8e2ce3c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:XAVSZ7HVEIW3EKI73SX6RRVODB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Anomaly Detection via Autoencoder Composite Features and NCE","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Austin J. Brockmeier, Yalin Liao","submitted_at":"2025-02-04T01:29:22Z","abstract_excerpt":"Unsupervised anomaly detection is a challenging task. Autoencoders (AEs) or generative models are often employed to model the data distribution of normal inputs and subsequently identify anomalous, out-of-distribution inputs by high reconstruction error or low likelihood, respectively. However, AEs may generalize and achieve small reconstruction errors on abnormal inputs. We propose a decoupled training approach for anomaly detection that both an AE and a likelihood model trained with noise contrastive estimation (NCE). After training the AE, NCE estimates a probability density function, to se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01920","kind":"arxiv","version":2},"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/2502.01920/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":"2026-07-05T11:19:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7MmsIcqah7p1WJ70+p3PGmLyqhSGBk7ESLrPy+bw1pUNiZjwMbjEBTNJN1uk6hxSh8CEuw/6X3sLVsgKi3+BDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T09:08:00.287029Z"},"content_sha256":"1c4d2ab1f81963f89b1b1e16e112b9a2e90632ad297a43904d9010ba1c0ee4cb","schema_version":"1.0","event_id":"sha256:1c4d2ab1f81963f89b1b1e16e112b9a2e90632ad297a43904d9010ba1c0ee4cb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XAVSZ7HVEIW3EKI73SX6RRVODB/bundle.json","state_url":"https://pith.science/pith/XAVSZ7HVEIW3EKI73SX6RRVODB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XAVSZ7HVEIW3EKI73SX6RRVODB/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":"2026-08-13T09:08:00Z","links":{"resolver":"https://pith.science/pith/XAVSZ7HVEIW3EKI73SX6RRVODB","bundle":"https://pith.science/pith/XAVSZ7HVEIW3EKI73SX6RRVODB/bundle.json","state":"https://pith.science/pith/XAVSZ7HVEIW3EKI73SX6RRVODB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XAVSZ7HVEIW3EKI73SX6RRVODB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:XAVSZ7HVEIW3EKI73SX6RRVODB","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":"b86c6e69d53edca40cc0e4b83cbe251a7f8f2c59beba80d8b68a911b0e26f5f1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-04T01:29:22Z","title_canon_sha256":"9afacf53c1a89fd97927d934faabca8f1aa76206b5f6e198c50abff7ca99cb52"},"schema_version":"1.0","source":{"id":"2502.01920","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.01920","created_at":"2026-07-05T11:19:51Z"},{"alias_kind":"arxiv_version","alias_value":"2502.01920v2","created_at":"2026-07-05T11:19:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01920","created_at":"2026-07-05T11:19:51Z"},{"alias_kind":"pith_short_12","alias_value":"XAVSZ7HVEIW3","created_at":"2026-07-05T11:19:51Z"},{"alias_kind":"pith_short_16","alias_value":"XAVSZ7HVEIW3EKI7","created_at":"2026-07-05T11:19:51Z"},{"alias_kind":"pith_short_8","alias_value":"XAVSZ7HV","created_at":"2026-07-05T11:19:51Z"}],"graph_snapshots":[{"event_id":"sha256:1c4d2ab1f81963f89b1b1e16e112b9a2e90632ad297a43904d9010ba1c0ee4cb","target":"graph","created_at":"2026-07-05T11:19:51Z","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/2502.01920/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Unsupervised anomaly detection is a challenging task. Autoencoders (AEs) or generative models are often employed to model the data distribution of normal inputs and subsequently identify anomalous, out-of-distribution inputs by high reconstruction error or low likelihood, respectively. However, AEs may generalize and achieve small reconstruction errors on abnormal inputs. We propose a decoupled training approach for anomaly detection that both an AE and a likelihood model trained with noise contrastive estimation (NCE). After training the AE, NCE estimates a probability density function, to se","authors_text":"Austin J. Brockmeier, Yalin Liao","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01920","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":"sha256:3509ef0fde1c4c92adc70bc001cd09158ac3abdea3070acd88f691dcf8e2ce3c","target":"record","created_at":"2026-07-05T11:19:51Z","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":"b86c6e69d53edca40cc0e4b83cbe251a7f8f2c59beba80d8b68a911b0e26f5f1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-04T01:29:22Z","title_canon_sha256":"9afacf53c1a89fd97927d934faabca8f1aa76206b5f6e198c50abff7ca99cb52"},"schema_version":"1.0","source":{"id":"2502.01920","kind":"arxiv","version":2}},"canonical_sha256":"b82b2cfcf5222db2291fdcafe8c6ae187f3cf161109e6eaf3c1bb59aa27e5426","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b82b2cfcf5222db2291fdcafe8c6ae187f3cf161109e6eaf3c1bb59aa27e5426","first_computed_at":"2026-07-05T11:19:51.109020Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:19:51.109020Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"leMAOUGL17jlVrix28EaR5togHovgcLEN5dr8GnU6RG+HF5dCgat9sEcxVuTAbBgRwvzuCst3Ms+/rsn5mOJCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:19:51.109433Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.01920","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3509ef0fde1c4c92adc70bc001cd09158ac3abdea3070acd88f691dcf8e2ce3c","sha256:1c4d2ab1f81963f89b1b1e16e112b9a2e90632ad297a43904d9010ba1c0ee4cb"],"state_sha256":"c4f2be7bcfb2674eb000bafa02d5427c50c133de2cc48522df73a775e9e9a2e5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BHGg8nKaFxeacmBgHa41auotFTUmLM9P2nCEPFTalteETqDCkEINSl702dg8jDoujlk9HIK4QdgAR+mvSu+NBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T09:08:00.291758Z","bundle_sha256":"20ca8448788620b945536b4b5c9410fa626662633ab18eb21f449b9462791ab5"}}