{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:WZDEVFU4MJ2DOX27NBG62VH7N4","short_pith_number":"pith:WZDEVFU4","canonical_record":{"source":{"id":"2210.13390","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ML","submitted_at":"2022-10-24T16:43:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0dff2a21c50bbdad528a8c61b6e2feb04f250496af656a24c32602dd5dee8429","abstract_canon_sha256":"286bcd0c4ba55dfedbc6039ca7784332dbee310751dd3c00fd348efbf3dab975"},"schema_version":"1.0"},"canonical_sha256":"b6464a969c6274375f5f684ded54ff6f0f920b5ba2e8b0a18a0fded851836aef","source":{"kind":"arxiv","id":"2210.13390","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.13390","created_at":"2026-07-05T05:09:44Z"},{"alias_kind":"arxiv_version","alias_value":"2210.13390v1","created_at":"2026-07-05T05:09:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.13390","created_at":"2026-07-05T05:09:44Z"},{"alias_kind":"pith_short_12","alias_value":"WZDEVFU4MJ2D","created_at":"2026-07-05T05:09:44Z"},{"alias_kind":"pith_short_16","alias_value":"WZDEVFU4MJ2DOX27","created_at":"2026-07-05T05:09:44Z"},{"alias_kind":"pith_short_8","alias_value":"WZDEVFU4","created_at":"2026-07-05T05:09:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:WZDEVFU4MJ2DOX27NBG62VH7N4","target":"record","payload":{"canonical_record":{"source":{"id":"2210.13390","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ML","submitted_at":"2022-10-24T16:43:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0dff2a21c50bbdad528a8c61b6e2feb04f250496af656a24c32602dd5dee8429","abstract_canon_sha256":"286bcd0c4ba55dfedbc6039ca7784332dbee310751dd3c00fd348efbf3dab975"},"schema_version":"1.0"},"canonical_sha256":"b6464a969c6274375f5f684ded54ff6f0f920b5ba2e8b0a18a0fded851836aef","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:09:44.907864Z","signature_b64":"MCDewGxd7ce6qv8g8NpUz+2SXyYv3tm2+2wzn0Yjad8Sph3wAZuU8+MF32wciMu8CDCtRzSbcD886WSpSHFLAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6464a969c6274375f5f684ded54ff6f0f920b5ba2e8b0a18a0fded851836aef","last_reissued_at":"2026-07-05T05:09:44.907453Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:09:44.907453Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.13390","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:09:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Pbi7IDtMMs8sIJe7Ep44OSaWYYvjpPbX7zU5ouV3YZmTdOiC8GwPoQYl+5LLj/LbRU24iwVY56mL3waPmar2Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T06:15:02.050663Z"},"content_sha256":"c5d88f9d1863ca47d73bfb3bed886c197a79f14d71830e9ec4d37194371e0128","schema_version":"1.0","event_id":"sha256:c5d88f9d1863ca47d73bfb3bed886c197a79f14d71830e9ec4d37194371e0128"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:WZDEVFU4MJ2DOX27NBG62VH7N4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On the failure of variational score matching for VAE models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Li Kevin Wenliang","submitted_at":"2022-10-24T16:43:04Z","abstract_excerpt":"Score matching (SM) is a convenient method for training flexible probabilistic models, which is often preferred over the traditional maximum-likelihood (ML) approach. However, these models are less interpretable than normalized models; as such, training robustness is in general difficult to assess. We present a critical study of existing variational SM objectives, showing catastrophic failure on a wide range of datasets and network architectures. Our theoretical insights on the objectives emerge directly from their equivalent autoencoding losses when optimizing variational autoencoder (VAE) mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.13390","kind":"arxiv","version":1},"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/2210.13390/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":"2026-07-05T05:09:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lT802mBc193PA5b5EjyIlk1SUurbwEerpeVSyvwfexamRD+LdxTgT+/p2fYOptgytRapb750Np7IYsm7xtcFBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T06:15:02.051195Z"},"content_sha256":"7286f5e12d60d1368cfb20fafb5ea15fc6c5c7767f6d727e5fe09488e5a58764","schema_version":"1.0","event_id":"sha256:7286f5e12d60d1368cfb20fafb5ea15fc6c5c7767f6d727e5fe09488e5a58764"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WZDEVFU4MJ2DOX27NBG62VH7N4/bundle.json","state_url":"https://pith.science/pith/WZDEVFU4MJ2DOX27NBG62VH7N4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WZDEVFU4MJ2DOX27NBG62VH7N4/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":"2026-08-08T06:15:02Z","links":{"resolver":"https://pith.science/pith/WZDEVFU4MJ2DOX27NBG62VH7N4","bundle":"https://pith.science/pith/WZDEVFU4MJ2DOX27NBG62VH7N4/bundle.json","state":"https://pith.science/pith/WZDEVFU4MJ2DOX27NBG62VH7N4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WZDEVFU4MJ2DOX27NBG62VH7N4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:WZDEVFU4MJ2DOX27NBG62VH7N4","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":"286bcd0c4ba55dfedbc6039ca7784332dbee310751dd3c00fd348efbf3dab975","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ML","submitted_at":"2022-10-24T16:43:04Z","title_canon_sha256":"0dff2a21c50bbdad528a8c61b6e2feb04f250496af656a24c32602dd5dee8429"},"schema_version":"1.0","source":{"id":"2210.13390","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.13390","created_at":"2026-07-05T05:09:44Z"},{"alias_kind":"arxiv_version","alias_value":"2210.13390v1","created_at":"2026-07-05T05:09:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.13390","created_at":"2026-07-05T05:09:44Z"},{"alias_kind":"pith_short_12","alias_value":"WZDEVFU4MJ2D","created_at":"2026-07-05T05:09:44Z"},{"alias_kind":"pith_short_16","alias_value":"WZDEVFU4MJ2DOX27","created_at":"2026-07-05T05:09:44Z"},{"alias_kind":"pith_short_8","alias_value":"WZDEVFU4","created_at":"2026-07-05T05:09:44Z"}],"graph_snapshots":[{"event_id":"sha256:7286f5e12d60d1368cfb20fafb5ea15fc6c5c7767f6d727e5fe09488e5a58764","target":"graph","created_at":"2026-07-05T05:09:44Z","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/2210.13390/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Score matching (SM) is a convenient method for training flexible probabilistic models, which is often preferred over the traditional maximum-likelihood (ML) approach. However, these models are less interpretable than normalized models; as such, training robustness is in general difficult to assess. We present a critical study of existing variational SM objectives, showing catastrophic failure on a wide range of datasets and network architectures. Our theoretical insights on the objectives emerge directly from their equivalent autoencoding losses when optimizing variational autoencoder (VAE) mo","authors_text":"Li Kevin Wenliang","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ML","submitted_at":"2022-10-24T16:43:04Z","title":"On the failure of variational score matching for VAE models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.13390","kind":"arxiv","version":1},"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:c5d88f9d1863ca47d73bfb3bed886c197a79f14d71830e9ec4d37194371e0128","target":"record","created_at":"2026-07-05T05:09:44Z","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":"286bcd0c4ba55dfedbc6039ca7784332dbee310751dd3c00fd348efbf3dab975","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ML","submitted_at":"2022-10-24T16:43:04Z","title_canon_sha256":"0dff2a21c50bbdad528a8c61b6e2feb04f250496af656a24c32602dd5dee8429"},"schema_version":"1.0","source":{"id":"2210.13390","kind":"arxiv","version":1}},"canonical_sha256":"b6464a969c6274375f5f684ded54ff6f0f920b5ba2e8b0a18a0fded851836aef","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b6464a969c6274375f5f684ded54ff6f0f920b5ba2e8b0a18a0fded851836aef","first_computed_at":"2026-07-05T05:09:44.907453Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:09:44.907453Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MCDewGxd7ce6qv8g8NpUz+2SXyYv3tm2+2wzn0Yjad8Sph3wAZuU8+MF32wciMu8CDCtRzSbcD886WSpSHFLAw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:09:44.907864Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.13390","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c5d88f9d1863ca47d73bfb3bed886c197a79f14d71830e9ec4d37194371e0128","sha256:7286f5e12d60d1368cfb20fafb5ea15fc6c5c7767f6d727e5fe09488e5a58764"],"state_sha256":"3dd6988227d85a403aa83a60559fa0083bf1afa0cc04e9faddc2cb5904fe3b43"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M3/ARCXGshgU1MT/bp+dsbfcPg0lQU+qtjRpMD5qw83SVoiCakMpYPqTL4rdRMLwAaaML9ueOFZIgQojIPyUDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T06:15:02.054781Z","bundle_sha256":"c730223166b6872b7c49065ae889cd54f03268256745e859fb4327185d719fcb"}}