{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:VTYTMET2WS25J6E5KEEZRNEIZY","short_pith_number":"pith:VTYTMET2","canonical_record":{"source":{"id":"2010.13132","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-25T15:10:31Z","cross_cats_sorted":[],"title_canon_sha256":"ec7efdb8c2ecad23030f225ab0e5bc2d5848ae6a89a2efe1fb60b1661b4d3e5b","abstract_canon_sha256":"531bd43c3d12e0d67eb9be2319714033f8c73b3cb2c1c41f15ac7155972a9e1f"},"schema_version":"1.0"},"canonical_sha256":"acf136127ab4b5d4f89d510998b488ce22716d46eb0e4dbfb86b9bc9df1127c0","source":{"kind":"arxiv","id":"2010.13132","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.13132","created_at":"2026-07-05T02:25:27Z"},{"alias_kind":"arxiv_version","alias_value":"2010.13132v3","created_at":"2026-07-05T02:25:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.13132","created_at":"2026-07-05T02:25:27Z"},{"alias_kind":"pith_short_12","alias_value":"VTYTMET2WS25","created_at":"2026-07-05T02:25:27Z"},{"alias_kind":"pith_short_16","alias_value":"VTYTMET2WS25J6E5","created_at":"2026-07-05T02:25:27Z"},{"alias_kind":"pith_short_8","alias_value":"VTYTMET2","created_at":"2026-07-05T02:25:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:VTYTMET2WS25J6E5KEEZRNEIZY","target":"record","payload":{"canonical_record":{"source":{"id":"2010.13132","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-25T15:10:31Z","cross_cats_sorted":[],"title_canon_sha256":"ec7efdb8c2ecad23030f225ab0e5bc2d5848ae6a89a2efe1fb60b1661b4d3e5b","abstract_canon_sha256":"531bd43c3d12e0d67eb9be2319714033f8c73b3cb2c1c41f15ac7155972a9e1f"},"schema_version":"1.0"},"canonical_sha256":"acf136127ab4b5d4f89d510998b488ce22716d46eb0e4dbfb86b9bc9df1127c0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:25:27.324308Z","signature_b64":"KMpVmc7HniSEuZA4sv41VInA9C6u/+LQ5xPx8kMGm76N3/dQrnP5VArjwPDdpIpwnNOiDp1TeuhoJq9gr4fnCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"acf136127ab4b5d4f89d510998b488ce22716d46eb0e4dbfb86b9bc9df1127c0","last_reissued_at":"2026-07-05T02:25:27.323840Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:25:27.323840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.13132","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":"2026-07-05T02:25:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZJEl6Kz4VpNlPOksCchod5T0yP+B1P4MZhoDs15D75kOvR+VV4RkHFkLtmwwCQqHOj6M8QcZfmR3on5WOR4GDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T17:51:41.392011Z"},"content_sha256":"92b7125b829279729a1645ad74d73d599d75bcf524b993ea444be9d78c9d61ef","schema_version":"1.0","event_id":"sha256:92b7125b829279729a1645ad74d73d599d75bcf524b993ea444be9d78c9d61ef"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:VTYTMET2WS25J6E5KEEZRNEIZY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multiscale Score Matching for Out-of-Distribution Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ahsan Mahmood, Junier Oliva, Martin Styner","submitted_at":"2020-10-25T15:10:31Z","abstract_excerpt":"We present a new methodology for detecting out-of-distribution (OOD) images by utilizing norms of the score estimates at multiple noise scales. A score is defined to be the gradient of the log density with respect to the input data. Our methodology is completely unsupervised and follows a straight forward training scheme. First, we train a deep network to estimate scores for levels of noise. Once trained, we calculate the noisy score estimates for N in-distribution samples and take the L2-norms across the input dimensions (resulting in an NxL matrix). Then we train an auxiliary model (such as "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.13132","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/2010.13132/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":"2026-07-05T02:25:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DyS4GiOE8nyL+5SY+TfCzCaZtEXCXV+VOpHL4Nh3HiYhgfqIGlmBn5TrUVgcWOD3M4QdfB5/lYvNIYfzY3GoCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T17:51:41.392723Z"},"content_sha256":"301b4352f30e940475e8007f34114f78509af411786f94a04e63d5d77ced4aba","schema_version":"1.0","event_id":"sha256:301b4352f30e940475e8007f34114f78509af411786f94a04e63d5d77ced4aba"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VTYTMET2WS25J6E5KEEZRNEIZY/bundle.json","state_url":"https://pith.science/pith/VTYTMET2WS25J6E5KEEZRNEIZY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VTYTMET2WS25J6E5KEEZRNEIZY/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":"2026-08-09T17:51:41Z","links":{"resolver":"https://pith.science/pith/VTYTMET2WS25J6E5KEEZRNEIZY","bundle":"https://pith.science/pith/VTYTMET2WS25J6E5KEEZRNEIZY/bundle.json","state":"https://pith.science/pith/VTYTMET2WS25J6E5KEEZRNEIZY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VTYTMET2WS25J6E5KEEZRNEIZY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:VTYTMET2WS25J6E5KEEZRNEIZY","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":"531bd43c3d12e0d67eb9be2319714033f8c73b3cb2c1c41f15ac7155972a9e1f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-25T15:10:31Z","title_canon_sha256":"ec7efdb8c2ecad23030f225ab0e5bc2d5848ae6a89a2efe1fb60b1661b4d3e5b"},"schema_version":"1.0","source":{"id":"2010.13132","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.13132","created_at":"2026-07-05T02:25:27Z"},{"alias_kind":"arxiv_version","alias_value":"2010.13132v3","created_at":"2026-07-05T02:25:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.13132","created_at":"2026-07-05T02:25:27Z"},{"alias_kind":"pith_short_12","alias_value":"VTYTMET2WS25","created_at":"2026-07-05T02:25:27Z"},{"alias_kind":"pith_short_16","alias_value":"VTYTMET2WS25J6E5","created_at":"2026-07-05T02:25:27Z"},{"alias_kind":"pith_short_8","alias_value":"VTYTMET2","created_at":"2026-07-05T02:25:27Z"}],"graph_snapshots":[{"event_id":"sha256:301b4352f30e940475e8007f34114f78509af411786f94a04e63d5d77ced4aba","target":"graph","created_at":"2026-07-05T02:25:27Z","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/2010.13132/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present a new methodology for detecting out-of-distribution (OOD) images by utilizing norms of the score estimates at multiple noise scales. A score is defined to be the gradient of the log density with respect to the input data. Our methodology is completely unsupervised and follows a straight forward training scheme. First, we train a deep network to estimate scores for levels of noise. Once trained, we calculate the noisy score estimates for N in-distribution samples and take the L2-norms across the input dimensions (resulting in an NxL matrix). Then we train an auxiliary model (such as ","authors_text":"Ahsan Mahmood, Junier Oliva, Martin Styner","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-25T15:10:31Z","title":"Multiscale Score Matching for Out-of-Distribution Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.13132","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":"sha256:92b7125b829279729a1645ad74d73d599d75bcf524b993ea444be9d78c9d61ef","target":"record","created_at":"2026-07-05T02:25:27Z","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":"531bd43c3d12e0d67eb9be2319714033f8c73b3cb2c1c41f15ac7155972a9e1f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-25T15:10:31Z","title_canon_sha256":"ec7efdb8c2ecad23030f225ab0e5bc2d5848ae6a89a2efe1fb60b1661b4d3e5b"},"schema_version":"1.0","source":{"id":"2010.13132","kind":"arxiv","version":3}},"canonical_sha256":"acf136127ab4b5d4f89d510998b488ce22716d46eb0e4dbfb86b9bc9df1127c0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"acf136127ab4b5d4f89d510998b488ce22716d46eb0e4dbfb86b9bc9df1127c0","first_computed_at":"2026-07-05T02:25:27.323840Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:25:27.323840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KMpVmc7HniSEuZA4sv41VInA9C6u/+LQ5xPx8kMGm76N3/dQrnP5VArjwPDdpIpwnNOiDp1TeuhoJq9gr4fnCg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:25:27.324308Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.13132","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:92b7125b829279729a1645ad74d73d599d75bcf524b993ea444be9d78c9d61ef","sha256:301b4352f30e940475e8007f34114f78509af411786f94a04e63d5d77ced4aba"],"state_sha256":"acb234c3d91f0924ab76bed04ce1db367c7cd8e1319ac14252208345bce51789"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uleVlUubZ4sAaz2sSYwQGO5lp9MS52k5mctmPPTwlhjhVqgB4Q0FyPOoza78l68+UB7mysZBhLcaWysCwlp3Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T17:51:41.397985Z","bundle_sha256":"7176da16be75ea8137ac4fc58194a6666344207d69cdaa29c851bd2c965ba4c6"}}