{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:B33NVUGIVHQ4KL4RRFLLMW3XKK","short_pith_number":"pith:B33NVUGI","schema_version":"1.0","canonical_sha256":"0ef6dad0c8a9e1c52f918956b65b7752b0dc6bdee6baf9f3d8ff35380ed3bc96","source":{"kind":"arxiv","id":"1912.13170","version":1},"attestation_state":"computed","paper":{"title":"Schr\\\"odinger Bridge Samplers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.CO","authors_text":"Arnaud Doucet, Espen Bernton, Jeremy Heng, Pierre E. Jacob","submitted_at":"2019-12-31T04:49:30Z","abstract_excerpt":"Consider a reference Markov process with initial distribution $\\pi_{0}$ and transition kernels $\\{M_{t}\\}_{t\\in[1:T]}$, for some $T\\in\\mathbb{N}$. Assume that you are given distribution $\\pi_{T}$, which is not equal to the marginal distribution of the reference process at time $T$. In this scenario, Schr\\\"odinger addressed the problem of identifying the Markov process with initial distribution $\\pi_{0}$ and terminal distribution equal to $\\pi_{T}$ which is the closest to the reference process in terms of Kullback--Leibler divergence. This special case of the so-called Schr\\\"odinger bridge prob"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1912.13170","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-12-31T04:49:30Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a64781de625a29f8cc0960b3fc5fc2141dd6a5d41a25bd52d70eaed451e85ed6","abstract_canon_sha256":"1e99cea43d7a145ae43287d3c14d772250f94069f77a22f0cdd8b76ed634fd4b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:29:06.654381Z","signature_b64":"4Xk6kVuiyNpZl7gEHk1miJTg/didhnMcmJ9/mmZyZ5+6eaoCKYOLyOOPa6knrTFbntwdQF3oXuwzpRsEktVsCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ef6dad0c8a9e1c52f918956b65b7752b0dc6bdee6baf9f3d8ff35380ed3bc96","last_reissued_at":"2026-07-05T00:29:06.653910Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:29:06.653910Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Schr\\\"odinger Bridge Samplers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.CO","authors_text":"Arnaud Doucet, Espen Bernton, Jeremy Heng, Pierre E. Jacob","submitted_at":"2019-12-31T04:49:30Z","abstract_excerpt":"Consider a reference Markov process with initial distribution $\\pi_{0}$ and transition kernels $\\{M_{t}\\}_{t\\in[1:T]}$, for some $T\\in\\mathbb{N}$. Assume that you are given distribution $\\pi_{T}$, which is not equal to the marginal distribution of the reference process at time $T$. In this scenario, Schr\\\"odinger addressed the problem of identifying the Markov process with initial distribution $\\pi_{0}$ and terminal distribution equal to $\\pi_{T}$ which is the closest to the reference process in terms of Kullback--Leibler divergence. This special case of the so-called Schr\\\"odinger bridge prob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.13170","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/1912.13170/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1912.13170","created_at":"2026-07-05T00:29:06.653964+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.13170v1","created_at":"2026-07-05T00:29:06.653964+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.13170","created_at":"2026-07-05T00:29:06.653964+00:00"},{"alias_kind":"pith_short_12","alias_value":"B33NVUGIVHQ4","created_at":"2026-07-05T00:29:06.653964+00:00"},{"alias_kind":"pith_short_16","alias_value":"B33NVUGIVHQ4KL4R","created_at":"2026-07-05T00:29:06.653964+00:00"},{"alias_kind":"pith_short_8","alias_value":"B33NVUGI","created_at":"2026-07-05T00:29:06.653964+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26314","citing_title":"Sampling Using Hybrid Stochastic Dynamics","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11554","citing_title":"Recovering the initial condition and physical coefficients in a nonlinear PDE model of cell invasion","ref_index":74,"is_internal_anchor":false},{"citing_arxiv_id":"2512.18928","citing_title":"The Ensemble Schr{\\\"o}dinger Bridge filter for Nonlinear Data Assimilation","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/B33NVUGIVHQ4KL4RRFLLMW3XKK","json":"https://pith.science/pith/B33NVUGIVHQ4KL4RRFLLMW3XKK.json","graph_json":"https://pith.science/api/pith-number/B33NVUGIVHQ4KL4RRFLLMW3XKK/graph.json","events_json":"https://pith.science/api/pith-number/B33NVUGIVHQ4KL4RRFLLMW3XKK/events.json","paper":"https://pith.science/paper/B33NVUGI"},"agent_actions":{"view_html":"https://pith.science/pith/B33NVUGIVHQ4KL4RRFLLMW3XKK","download_json":"https://pith.science/pith/B33NVUGIVHQ4KL4RRFLLMW3XKK.json","view_paper":"https://pith.science/paper/B33NVUGI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.13170&json=true","fetch_graph":"https://pith.science/api/pith-number/B33NVUGIVHQ4KL4RRFLLMW3XKK/graph.json","fetch_events":"https://pith.science/api/pith-number/B33NVUGIVHQ4KL4RRFLLMW3XKK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B33NVUGIVHQ4KL4RRFLLMW3XKK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B33NVUGIVHQ4KL4RRFLLMW3XKK/action/storage_attestation","attest_author":"https://pith.science/pith/B33NVUGIVHQ4KL4RRFLLMW3XKK/action/author_attestation","sign_citation":"https://pith.science/pith/B33NVUGIVHQ4KL4RRFLLMW3XKK/action/citation_signature","submit_replication":"https://pith.science/pith/B33NVUGIVHQ4KL4RRFLLMW3XKK/action/replication_record"}},"created_at":"2026-07-05T00:29:06.653964+00:00","updated_at":"2026-07-05T00:29:06.653964+00:00"}