{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:QWPLF6VDOQPXANRRZL62UUO4BI","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":"7ddf7162aef9d5056782134a84005a9ae6fd30623c43ca9bef908cfc0500b6a0","cross_cats_sorted":["cs.LG","math.ST","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-02-23T12:30:20Z","title_canon_sha256":"c2d1240879feb4a692d5d4c40be6e4af9eec44aecd461cdddecaac6c81f06cea"},"schema_version":"1.0","source":{"id":"2402.15285","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.15285","created_at":"2026-07-05T07:48:38Z"},{"alias_kind":"arxiv_version","alias_value":"2402.15285v1","created_at":"2026-07-05T07:48:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.15285","created_at":"2026-07-05T07:48:38Z"},{"alias_kind":"pith_short_12","alias_value":"QWPLF6VDOQPX","created_at":"2026-07-05T07:48:38Z"},{"alias_kind":"pith_short_16","alias_value":"QWPLF6VDOQPXANRR","created_at":"2026-07-05T07:48:38Z"},{"alias_kind":"pith_short_8","alias_value":"QWPLF6VD","created_at":"2026-07-05T07:48:38Z"}],"graph_snapshots":[{"event_id":"sha256:8fa506338af649506f21ef1691823f6d6dbc862bb26f6319a0d1d88744637b0e","target":"graph","created_at":"2026-07-05T07:48:38Z","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/2402.15285/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Sampling from probability densities is a common challenge in fields such as Uncertainty Quantification (UQ) and Generative Modelling (GM). In GM in particular, the use of reverse-time diffusion processes depending on the log-densities of Ornstein-Uhlenbeck forward processes are a popular sampling tool. In Berner et al. [2022] the authors point out that these log-densities can be obtained by solution of a \\textit{Hamilton-Jacobi-Bellman} (HJB) equation known from stochastic optimal control. While this HJB equation is usually treated with indirect methods such as policy iteration and unsupervise","authors_text":"Claudia Schillings, David Sommer, Martin Eigel, Max Kirstein, Robert Gruhlke","cross_cats":["cs.LG","math.ST","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-02-23T12:30:20Z","title":"Generative Modelling with Tensor Train approximations of Hamilton--Jacobi--Bellman equations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.15285","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:eb8ed423af5439a137507099c274d3a653616db1e2063de9314818c828eda452","target":"record","created_at":"2026-07-05T07:48:38Z","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":"7ddf7162aef9d5056782134a84005a9ae6fd30623c43ca9bef908cfc0500b6a0","cross_cats_sorted":["cs.LG","math.ST","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-02-23T12:30:20Z","title_canon_sha256":"c2d1240879feb4a692d5d4c40be6e4af9eec44aecd461cdddecaac6c81f06cea"},"schema_version":"1.0","source":{"id":"2402.15285","kind":"arxiv","version":1}},"canonical_sha256":"859eb2faa3741f703631cafdaa51dc0a1b75ceb5f9ec22790505954848ed7b61","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"859eb2faa3741f703631cafdaa51dc0a1b75ceb5f9ec22790505954848ed7b61","first_computed_at":"2026-07-05T07:48:38.704598Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:48:38.704598Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uvIXnTUNe3gJfRBhy23OrViQRRGwdv9tQqCIrPQW3MYns8ItDzgILaQ/gjrLyfT8DkmpccgmgV4oYZbzmZVoCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:48:38.705048Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.15285","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eb8ed423af5439a137507099c274d3a653616db1e2063de9314818c828eda452","sha256:8fa506338af649506f21ef1691823f6d6dbc862bb26f6319a0d1d88744637b0e"],"state_sha256":"73f22d96cfe7332c37f31e599a929cb0d6de1c7e951780f116b8b51a673eb5f8"}