{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:THSB4QLSDLJOUWKEQHSA6WQ7WN","short_pith_number":"pith:THSB4QLS","schema_version":"1.0","canonical_sha256":"99e41e41721ad2ea594481e40f5a1fb371735b3338650ad2feb104128b641b6b","source":{"kind":"arxiv","id":"2410.07550","version":2},"attestation_state":"computed","paper":{"title":"Conditional Lagrangian Wasserstein Flow for Time Series Imputation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Dalin Zhang, Weizhu Qian, Yan Zhao, Yunyao Cheng","submitted_at":"2024-10-10T02:46:28Z","abstract_excerpt":"Time series imputation is important for numerous real-world applications. To overcome the limitations of diffusion model-based imputation methods, e.g., slow convergence in inference, we propose a novel method for time series imputation in this work, called Conditional Lagrangian Wasserstein Flow (CLWF). Following the principle of least action in Lagrangian mechanics, we learn the velocity by minimizing the corresponding kinetic energy. Moreover, to enhance the model's performance, we estimate the gradient of a task-specific potential function using a time-dependent denoising autoencoder and i"},"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":"2410.07550","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-10T02:46:28Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"62c74e7c653002b4c7449d5e2e2473238677f3f7eb18ee7c1f8cedc041e11a89","abstract_canon_sha256":"5a474e0f770d7a4f4f237f688a2d7589625b15a2a3c6c50b5e9166841af3bbec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:59:25.191522Z","signature_b64":"JEkp3NpQk1Miy6euyUljMKdTN7CuExB4K5Mu4uZCH0sEGKJCGgJ3TzEcS/uAezeRtQQEj+lZW6F7wJBvNxskCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"99e41e41721ad2ea594481e40f5a1fb371735b3338650ad2feb104128b641b6b","last_reissued_at":"2026-07-05T10:59:25.191006Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:59:25.191006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conditional Lagrangian Wasserstein Flow for Time Series Imputation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Dalin Zhang, Weizhu Qian, Yan Zhao, Yunyao Cheng","submitted_at":"2024-10-10T02:46:28Z","abstract_excerpt":"Time series imputation is important for numerous real-world applications. To overcome the limitations of diffusion model-based imputation methods, e.g., slow convergence in inference, we propose a novel method for time series imputation in this work, called Conditional Lagrangian Wasserstein Flow (CLWF). Following the principle of least action in Lagrangian mechanics, we learn the velocity by minimizing the corresponding kinetic energy. Moreover, to enhance the model's performance, we estimate the gradient of a task-specific potential function using a time-dependent denoising autoencoder and i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.07550","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/2410.07550/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":"2410.07550","created_at":"2026-07-05T10:59:25.191059+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.07550v2","created_at":"2026-07-05T10:59:25.191059+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.07550","created_at":"2026-07-05T10:59:25.191059+00:00"},{"alias_kind":"pith_short_12","alias_value":"THSB4QLSDLJO","created_at":"2026-07-05T10:59:25.191059+00:00"},{"alias_kind":"pith_short_16","alias_value":"THSB4QLSDLJOUWKE","created_at":"2026-07-05T10:59:25.191059+00:00"},{"alias_kind":"pith_short_8","alias_value":"THSB4QLS","created_at":"2026-07-05T10:59:25.191059+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.06328","citing_title":"PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08550","citing_title":"A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08550","citing_title":"A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08550","citing_title":"A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/THSB4QLSDLJOUWKEQHSA6WQ7WN","json":"https://pith.science/pith/THSB4QLSDLJOUWKEQHSA6WQ7WN.json","graph_json":"https://pith.science/api/pith-number/THSB4QLSDLJOUWKEQHSA6WQ7WN/graph.json","events_json":"https://pith.science/api/pith-number/THSB4QLSDLJOUWKEQHSA6WQ7WN/events.json","paper":"https://pith.science/paper/THSB4QLS"},"agent_actions":{"view_html":"https://pith.science/pith/THSB4QLSDLJOUWKEQHSA6WQ7WN","download_json":"https://pith.science/pith/THSB4QLSDLJOUWKEQHSA6WQ7WN.json","view_paper":"https://pith.science/paper/THSB4QLS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.07550&json=true","fetch_graph":"https://pith.science/api/pith-number/THSB4QLSDLJOUWKEQHSA6WQ7WN/graph.json","fetch_events":"https://pith.science/api/pith-number/THSB4QLSDLJOUWKEQHSA6WQ7WN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/THSB4QLSDLJOUWKEQHSA6WQ7WN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/THSB4QLSDLJOUWKEQHSA6WQ7WN/action/storage_attestation","attest_author":"https://pith.science/pith/THSB4QLSDLJOUWKEQHSA6WQ7WN/action/author_attestation","sign_citation":"https://pith.science/pith/THSB4QLSDLJOUWKEQHSA6WQ7WN/action/citation_signature","submit_replication":"https://pith.science/pith/THSB4QLSDLJOUWKEQHSA6WQ7WN/action/replication_record"}},"created_at":"2026-07-05T10:59:25.191059+00:00","updated_at":"2026-07-05T10:59:25.191059+00:00"}