{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ZTM2DKCUVLMU4KWIXTYMDSIGBO","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":"19abd16146c3dd88232a47005f685462514718cf2770d652274a3680f27c5bcb","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-31T13:29:19Z","title_canon_sha256":"334bec1842db6e33511f9f08e76d26c88963f40158899577dd91fde623ee505a"},"schema_version":"1.0","source":{"id":"2301.13622","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.13622","created_at":"2026-07-05T05:58:19Z"},{"alias_kind":"arxiv_version","alias_value":"2301.13622v2","created_at":"2026-07-05T05:58:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.13622","created_at":"2026-07-05T05:58:19Z"},{"alias_kind":"pith_short_12","alias_value":"ZTM2DKCUVLMU","created_at":"2026-07-05T05:58:19Z"},{"alias_kind":"pith_short_16","alias_value":"ZTM2DKCUVLMU4KWI","created_at":"2026-07-05T05:58:19Z"},{"alias_kind":"pith_short_8","alias_value":"ZTM2DKCU","created_at":"2026-07-05T05:58:19Z"}],"graph_snapshots":[{"event_id":"sha256:0559087f3edd642dc8deb62a592ae575cafeaedf3fa7b447f05e72fc04914dc8","target":"graph","created_at":"2026-07-05T05:58:19Z","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/2301.13622/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Joint machine learning models that allow synthesizing and classifying data often offer uneven performance between those tasks or are unstable to train. In this work, we depart from a set of empirical observations that indicate the usefulness of internal representations built by contemporary deep diffusion-based generative models not only for generating but also predicting. We then propose to extend the vanilla diffusion model with a classifier that allows for stable joint end-to-end training with shared parameterization between those objectives. The resulting joint diffusion model outperforms ","authors_text":"Jakub M. Tomczak, Kamil Deja, Tomasz Trzcinski","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-31T13:29:19Z","title":"Learning Data Representations with Joint Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.13622","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":"sha256:42bfa6f1cc6c1817c9486a5babc849dc183a4a508cf09632bccc048533a35653","target":"record","created_at":"2026-07-05T05:58:19Z","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":"19abd16146c3dd88232a47005f685462514718cf2770d652274a3680f27c5bcb","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-31T13:29:19Z","title_canon_sha256":"334bec1842db6e33511f9f08e76d26c88963f40158899577dd91fde623ee505a"},"schema_version":"1.0","source":{"id":"2301.13622","kind":"arxiv","version":2}},"canonical_sha256":"ccd9a1a854aad94e2ac8bcf0c1c9060b9b6066a6dda485f75eb0acf51716824e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ccd9a1a854aad94e2ac8bcf0c1c9060b9b6066a6dda485f75eb0acf51716824e","first_computed_at":"2026-07-05T05:58:19.493651Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:58:19.493651Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jjvW0vhbZJ0g24bGdkxmlYC2vt9Wpkn24wpHVT7Cyf6kfIKcPRqNH050Rs5dx+ahm3P20KT4puuO7loYG6bIDA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:58:19.494075Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.13622","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:42bfa6f1cc6c1817c9486a5babc849dc183a4a508cf09632bccc048533a35653","sha256:0559087f3edd642dc8deb62a592ae575cafeaedf3fa7b447f05e72fc04914dc8"],"state_sha256":"06a2d659aca311265a264188c8163986a641c5bcec97d8fd7c50125422d06f50"}