{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:KEWAV4Z6FUSAMP3IIKOXBFXTL4","short_pith_number":"pith:KEWAV4Z6","canonical_record":{"source":{"id":"2301.12686","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-30T06:27:48Z","cross_cats_sorted":["cs.AI","cs.CV","cs.SD","eess.AS"],"title_canon_sha256":"bac10e735ba1d7fcc1d19bd03bf97ade6a96cc08d9d1772554e8ab02e95880ed","abstract_canon_sha256":"a15e71b92a43c1e90e9393756882215ca0f8888b9867fef447d3893332fe9289"},"schema_version":"1.0"},"canonical_sha256":"512c0af33e2d24063f68429d7096f35f058fe976c8b5183f5366ba55e2dc91fa","source":{"kind":"arxiv","id":"2301.12686","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.12686","created_at":"2026-07-05T06:24:48Z"},{"alias_kind":"arxiv_version","alias_value":"2301.12686v2","created_at":"2026-07-05T06:24:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.12686","created_at":"2026-07-05T06:24:48Z"},{"alias_kind":"pith_short_12","alias_value":"KEWAV4Z6FUSA","created_at":"2026-07-05T06:24:48Z"},{"alias_kind":"pith_short_16","alias_value":"KEWAV4Z6FUSAMP3I","created_at":"2026-07-05T06:24:48Z"},{"alias_kind":"pith_short_8","alias_value":"KEWAV4Z6","created_at":"2026-07-05T06:24:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:KEWAV4Z6FUSAMP3IIKOXBFXTL4","target":"record","payload":{"canonical_record":{"source":{"id":"2301.12686","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-30T06:27:48Z","cross_cats_sorted":["cs.AI","cs.CV","cs.SD","eess.AS"],"title_canon_sha256":"bac10e735ba1d7fcc1d19bd03bf97ade6a96cc08d9d1772554e8ab02e95880ed","abstract_canon_sha256":"a15e71b92a43c1e90e9393756882215ca0f8888b9867fef447d3893332fe9289"},"schema_version":"1.0"},"canonical_sha256":"512c0af33e2d24063f68429d7096f35f058fe976c8b5183f5366ba55e2dc91fa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:24:48.313213Z","signature_b64":"062vhBB9TV901HWGeRsdzYNWrVsJf2/7O8bo12UH1g2L2veUu7SP0xTJ92Nr55hazAr1D6xdCwGGwFszWn0KDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"512c0af33e2d24063f68429d7096f35f058fe976c8b5183f5366ba55e2dc91fa","last_reissued_at":"2026-07-05T06:24:48.312709Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:24:48.312709Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2301.12686","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":"2026-07-05T06:24:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZWn3gQoOQJDJm3bBDArpWIxxwd7CF+XX6w4WqglxY5GQ8cI6GUDXvYnVORDLzemNtbhANqHvwi7RaZNIJtfYAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T14:48:30.294395Z"},"content_sha256":"f0ccfc34b13dfd5ce9a67b99580f0324f6883697a29bcc09d5f7de4b4b43c6d3","schema_version":"1.0","event_id":"sha256:f0ccfc34b13dfd5ce9a67b99580f0324f6883697a29bcc09d5f7de4b4b43c6d3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:KEWAV4Z6FUSAMP3IIKOXBFXTL4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.SD","eess.AS"],"primary_cat":"cs.LG","authors_text":"Chieh-Hsin Lai, Koichi Saito, Naoki Murata, Stefano Ermon, Toshimitsu Uesaka, Yuhta Takida, Yuki Mitsufuji","submitted_at":"2023-01-30T06:27:48Z","abstract_excerpt":"Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements. However, existing approaches require knowledge of the linear operator. In this paper, we propose GibbsDDRM, an extension of Denoising Diffusion Restoration Models (DDRM) to a blind setting in which the linear measurement operator is unknown. GibbsDDRM constructs a joint distribution of the data, measurements, and linear operator by using a pre-trained diffusion model for the data prior, and it solves the problem by"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.12686","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/2301.12686/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":"2026-07-05T06:24:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d2EIAXYpDA28JKXiODxJbmgSPokHIq4kDUajZ7+0gAlfAThbpH4gjOYETniNMMY+QluRU3Phc7t509h9nMf2AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T14:48:30.294778Z"},"content_sha256":"074fa248d8e0ec1ec88b1494b94a8af1d33b3feec6ce4d299c3eeb3b3aac1070","schema_version":"1.0","event_id":"sha256:074fa248d8e0ec1ec88b1494b94a8af1d33b3feec6ce4d299c3eeb3b3aac1070"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KEWAV4Z6FUSAMP3IIKOXBFXTL4/bundle.json","state_url":"https://pith.science/pith/KEWAV4Z6FUSAMP3IIKOXBFXTL4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KEWAV4Z6FUSAMP3IIKOXBFXTL4/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":"2026-07-06T14:48:30Z","links":{"resolver":"https://pith.science/pith/KEWAV4Z6FUSAMP3IIKOXBFXTL4","bundle":"https://pith.science/pith/KEWAV4Z6FUSAMP3IIKOXBFXTL4/bundle.json","state":"https://pith.science/pith/KEWAV4Z6FUSAMP3IIKOXBFXTL4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KEWAV4Z6FUSAMP3IIKOXBFXTL4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:KEWAV4Z6FUSAMP3IIKOXBFXTL4","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":"a15e71b92a43c1e90e9393756882215ca0f8888b9867fef447d3893332fe9289","cross_cats_sorted":["cs.AI","cs.CV","cs.SD","eess.AS"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-30T06:27:48Z","title_canon_sha256":"bac10e735ba1d7fcc1d19bd03bf97ade6a96cc08d9d1772554e8ab02e95880ed"},"schema_version":"1.0","source":{"id":"2301.12686","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.12686","created_at":"2026-07-05T06:24:48Z"},{"alias_kind":"arxiv_version","alias_value":"2301.12686v2","created_at":"2026-07-05T06:24:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.12686","created_at":"2026-07-05T06:24:48Z"},{"alias_kind":"pith_short_12","alias_value":"KEWAV4Z6FUSA","created_at":"2026-07-05T06:24:48Z"},{"alias_kind":"pith_short_16","alias_value":"KEWAV4Z6FUSAMP3I","created_at":"2026-07-05T06:24:48Z"},{"alias_kind":"pith_short_8","alias_value":"KEWAV4Z6","created_at":"2026-07-05T06:24:48Z"}],"graph_snapshots":[{"event_id":"sha256:074fa248d8e0ec1ec88b1494b94a8af1d33b3feec6ce4d299c3eeb3b3aac1070","target":"graph","created_at":"2026-07-05T06:24:48Z","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/2301.12686/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements. However, existing approaches require knowledge of the linear operator. In this paper, we propose GibbsDDRM, an extension of Denoising Diffusion Restoration Models (DDRM) to a blind setting in which the linear measurement operator is unknown. GibbsDDRM constructs a joint distribution of the data, measurements, and linear operator by using a pre-trained diffusion model for the data prior, and it solves the problem by","authors_text":"Chieh-Hsin Lai, Koichi Saito, Naoki Murata, Stefano Ermon, Toshimitsu Uesaka, Yuhta Takida, Yuki Mitsufuji","cross_cats":["cs.AI","cs.CV","cs.SD","eess.AS"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-30T06:27:48Z","title":"GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.12686","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:f0ccfc34b13dfd5ce9a67b99580f0324f6883697a29bcc09d5f7de4b4b43c6d3","target":"record","created_at":"2026-07-05T06:24:48Z","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":"a15e71b92a43c1e90e9393756882215ca0f8888b9867fef447d3893332fe9289","cross_cats_sorted":["cs.AI","cs.CV","cs.SD","eess.AS"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-30T06:27:48Z","title_canon_sha256":"bac10e735ba1d7fcc1d19bd03bf97ade6a96cc08d9d1772554e8ab02e95880ed"},"schema_version":"1.0","source":{"id":"2301.12686","kind":"arxiv","version":2}},"canonical_sha256":"512c0af33e2d24063f68429d7096f35f058fe976c8b5183f5366ba55e2dc91fa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"512c0af33e2d24063f68429d7096f35f058fe976c8b5183f5366ba55e2dc91fa","first_computed_at":"2026-07-05T06:24:48.312709Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:24:48.312709Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"062vhBB9TV901HWGeRsdzYNWrVsJf2/7O8bo12UH1g2L2veUu7SP0xTJ92Nr55hazAr1D6xdCwGGwFszWn0KDA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:24:48.313213Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.12686","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f0ccfc34b13dfd5ce9a67b99580f0324f6883697a29bcc09d5f7de4b4b43c6d3","sha256:074fa248d8e0ec1ec88b1494b94a8af1d33b3feec6ce4d299c3eeb3b3aac1070"],"state_sha256":"97f86fac1cb038a5c7923408ca7a51943df015c9f6638fde79fe6c29f6a03036"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"06oe/EHeDco7XJvwNmepo654J+QSl4WLamiw5luI+xA2TEY4YK/48f1rc34l7FMQWkGFS6NzZeqjDtDTI/66Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T14:48:30.296762Z","bundle_sha256":"3874ff7fe67345aaafe6182a7f8563b681271f7662b78f37f7d1b9a9b0da54a6"}}