{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:JPOPWQLOEA2RVU43NNQ7BVJIDW","short_pith_number":"pith:JPOPWQLO","canonical_record":{"source":{"id":"2203.14206","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-27T04:37:54Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"cad2dd96fd2fdee714d7ac952cb099074df6f7aa6df573334a9c19cab7e860f5","abstract_canon_sha256":"4d579930ad1eac27f02db3c190124069fc96feca360433dae62baad867823fcb"},"schema_version":"1.0"},"canonical_sha256":"4bdcfb416e20351ad39b6b61f0d5281d89ed7e143fda7a1cda0c223104f06621","source":{"kind":"arxiv","id":"2203.14206","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.14206","created_at":"2026-07-05T04:09:03Z"},{"alias_kind":"arxiv_version","alias_value":"2203.14206v1","created_at":"2026-07-05T04:09:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.14206","created_at":"2026-07-05T04:09:03Z"},{"alias_kind":"pith_short_12","alias_value":"JPOPWQLOEA2R","created_at":"2026-07-05T04:09:03Z"},{"alias_kind":"pith_short_16","alias_value":"JPOPWQLOEA2RVU43","created_at":"2026-07-05T04:09:03Z"},{"alias_kind":"pith_short_8","alias_value":"JPOPWQLO","created_at":"2026-07-05T04:09:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:JPOPWQLOEA2RVU43NNQ7BVJIDW","target":"record","payload":{"canonical_record":{"source":{"id":"2203.14206","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-27T04:37:54Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"cad2dd96fd2fdee714d7ac952cb099074df6f7aa6df573334a9c19cab7e860f5","abstract_canon_sha256":"4d579930ad1eac27f02db3c190124069fc96feca360433dae62baad867823fcb"},"schema_version":"1.0"},"canonical_sha256":"4bdcfb416e20351ad39b6b61f0d5281d89ed7e143fda7a1cda0c223104f06621","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:09:03.731436Z","signature_b64":"WC1h5/qUQ0QBBvXYlO0Fn4DCqs/ABAJYt2YwIhtba5hf4SomUAJkAaS2YIpqitjmGjT/1pkVInGaW7j+lUlXAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4bdcfb416e20351ad39b6b61f0d5281d89ed7e143fda7a1cda0c223104f06621","last_reissued_at":"2026-07-05T04:09:03.731039Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:09:03.731039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.14206","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":"2026-07-05T04:09:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Myt98yvLCsYvT6N0ZDez1zlB1NDd4FjNErQZKI0E07R003URoPU5snk7hgxn1v/0735mvEPBSD9Eo80xDjhhAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:09:46.297059Z"},"content_sha256":"8c4a609d8085bfabbbd05c049955cd08a3cc69fa56f9ec84bc8fbccbb6c0e42f","schema_version":"1.0","event_id":"sha256:8c4a609d8085bfabbbd05c049955cd08a3cc69fa56f9ec84bc8fbccbb6c0e42f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:JPOPWQLOEA2RVU43NNQ7BVJIDW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Denoising Likelihood Score Matching for Conditional Score-based Data Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bo-Wun Cheng, Chen-Hao Chao, Chia-Che Chang, Chia-Ping Chen, Chun-Yi Lee, Wei-Fang Sun, Yi-Chen Lo, Yu-Lin Chang, Yu-Lun Liu","submitted_at":"2022-03-27T04:37:54Z","abstract_excerpt":"Many existing conditional score-based data generation methods utilize Bayes' theorem to decompose the gradients of a log posterior density into a mixture of scores. These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a classifier. However, our analysis indicates that the training objectives for the classifier in these methods may lead to a serious score mismatch issue, which corresponds to the situation that the estimated scores deviate from the true ones. Such an issue causes the samples to be "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.14206","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/2203.14206/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":"2026-07-05T04:09:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vjJwVqRhkHt6y/f5N31HPH720OXtEgTDx5RQ22Am917qqhkgfXNDpRsSjIz3ZroLBOoli/rml0f2X86e9o06Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:09:46.298118Z"},"content_sha256":"e776af6f4977cd47e7fa3b2fde84442887e6265f1b0524f7cebc0ca6e6f53bbc","schema_version":"1.0","event_id":"sha256:e776af6f4977cd47e7fa3b2fde84442887e6265f1b0524f7cebc0ca6e6f53bbc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JPOPWQLOEA2RVU43NNQ7BVJIDW/bundle.json","state_url":"https://pith.science/pith/JPOPWQLOEA2RVU43NNQ7BVJIDW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JPOPWQLOEA2RVU43NNQ7BVJIDW/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":"2026-08-03T18:09:46Z","links":{"resolver":"https://pith.science/pith/JPOPWQLOEA2RVU43NNQ7BVJIDW","bundle":"https://pith.science/pith/JPOPWQLOEA2RVU43NNQ7BVJIDW/bundle.json","state":"https://pith.science/pith/JPOPWQLOEA2RVU43NNQ7BVJIDW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JPOPWQLOEA2RVU43NNQ7BVJIDW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:JPOPWQLOEA2RVU43NNQ7BVJIDW","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":"4d579930ad1eac27f02db3c190124069fc96feca360433dae62baad867823fcb","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-27T04:37:54Z","title_canon_sha256":"cad2dd96fd2fdee714d7ac952cb099074df6f7aa6df573334a9c19cab7e860f5"},"schema_version":"1.0","source":{"id":"2203.14206","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.14206","created_at":"2026-07-05T04:09:03Z"},{"alias_kind":"arxiv_version","alias_value":"2203.14206v1","created_at":"2026-07-05T04:09:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.14206","created_at":"2026-07-05T04:09:03Z"},{"alias_kind":"pith_short_12","alias_value":"JPOPWQLOEA2R","created_at":"2026-07-05T04:09:03Z"},{"alias_kind":"pith_short_16","alias_value":"JPOPWQLOEA2RVU43","created_at":"2026-07-05T04:09:03Z"},{"alias_kind":"pith_short_8","alias_value":"JPOPWQLO","created_at":"2026-07-05T04:09:03Z"}],"graph_snapshots":[{"event_id":"sha256:e776af6f4977cd47e7fa3b2fde84442887e6265f1b0524f7cebc0ca6e6f53bbc","target":"graph","created_at":"2026-07-05T04:09:03Z","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/2203.14206/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many existing conditional score-based data generation methods utilize Bayes' theorem to decompose the gradients of a log posterior density into a mixture of scores. These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a classifier. However, our analysis indicates that the training objectives for the classifier in these methods may lead to a serious score mismatch issue, which corresponds to the situation that the estimated scores deviate from the true ones. Such an issue causes the samples to be ","authors_text":"Bo-Wun Cheng, Chen-Hao Chao, Chia-Che Chang, Chia-Ping Chen, Chun-Yi Lee, Wei-Fang Sun, Yi-Chen Lo, Yu-Lin Chang, Yu-Lun Liu","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-27T04:37:54Z","title":"Denoising Likelihood Score Matching for Conditional Score-based Data Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.14206","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":"sha256:8c4a609d8085bfabbbd05c049955cd08a3cc69fa56f9ec84bc8fbccbb6c0e42f","target":"record","created_at":"2026-07-05T04:09:03Z","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":"4d579930ad1eac27f02db3c190124069fc96feca360433dae62baad867823fcb","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-27T04:37:54Z","title_canon_sha256":"cad2dd96fd2fdee714d7ac952cb099074df6f7aa6df573334a9c19cab7e860f5"},"schema_version":"1.0","source":{"id":"2203.14206","kind":"arxiv","version":1}},"canonical_sha256":"4bdcfb416e20351ad39b6b61f0d5281d89ed7e143fda7a1cda0c223104f06621","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4bdcfb416e20351ad39b6b61f0d5281d89ed7e143fda7a1cda0c223104f06621","first_computed_at":"2026-07-05T04:09:03.731039Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:09:03.731039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WC1h5/qUQ0QBBvXYlO0Fn4DCqs/ABAJYt2YwIhtba5hf4SomUAJkAaS2YIpqitjmGjT/1pkVInGaW7j+lUlXAA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:09:03.731436Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.14206","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8c4a609d8085bfabbbd05c049955cd08a3cc69fa56f9ec84bc8fbccbb6c0e42f","sha256:e776af6f4977cd47e7fa3b2fde84442887e6265f1b0524f7cebc0ca6e6f53bbc"],"state_sha256":"155a271d270ea93182098f65f0520d134a2442a708b6b7ac6a9e66041faf9042"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"86SfakXS+Sk7HliQS0P9b86vSTxAeB4rR2itF/s15UI0BLr/pNsV8jblBoSA5jJZiWX+kx4uW1pL/5NRg8AgDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T18:09:46.303982Z","bundle_sha256":"d380480b2529b37f0beb917be797977a463a7c3ab7b3ac50e8834a9702ca362c"}}