{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:5YLTYSWFYDSSJUB47V5IZZKVAZ","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":"30c3b6fd196e5ef100d600a890c1716f222249d2b38fbe5ec170139a3e30d0d2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-09T13:34:49Z","title_canon_sha256":"5cb3c8299911f5e5a55260d9cbbdfc8ff942daaadb8f5881e586c3dbfd153534"},"schema_version":"1.0","source":{"id":"1907.04155","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.04155","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"arxiv_version","alias_value":"1907.04155v5","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.04155","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"pith_short_12","alias_value":"5YLTYSWFYDSS","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"pith_short_16","alias_value":"5YLTYSWFYDSSJUB4","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"pith_short_8","alias_value":"5YLTYSWF","created_at":"2026-07-05T00:42:31Z"}],"graph_snapshots":[{"event_id":"sha256:263689665e6cbe553c72c16d70c63688836f35e00d33a8370d08994955099beb","target":"graph","created_at":"2026-07-05T00:42:31Z","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/1907.04155/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years. This raises the question whether deep learning methodologies can outperform classical data imputation methods in this domain. However, naive applications of deep learning fall short in giving reliable confidence estimates and lack interpretability. We propose a new deep sequential latent variable model for dimensionality reduction and data imputation. Our modeling assumption is simple and interpretable: the high dimensional time series has a l","authors_text":"Dmitry Baranchuk, Gunnar R\\\"atsch, Stephan Mandt, Vincent Fortuin","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-09T13:34:49Z","title":"GP-VAE: Deep Probabilistic Time Series Imputation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.04155","kind":"arxiv","version":5},"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:cddb24c3cd112125c695a0d832be60f7302cece3fc11001fc30a9d091e1f548f","target":"record","created_at":"2026-07-05T00:42:31Z","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":"30c3b6fd196e5ef100d600a890c1716f222249d2b38fbe5ec170139a3e30d0d2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-09T13:34:49Z","title_canon_sha256":"5cb3c8299911f5e5a55260d9cbbdfc8ff942daaadb8f5881e586c3dbfd153534"},"schema_version":"1.0","source":{"id":"1907.04155","kind":"arxiv","version":5}},"canonical_sha256":"ee173c4ac5c0e524d03cfd7a8ce555067446013a88cc31d2d5a77e2e6ea01f5d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ee173c4ac5c0e524d03cfd7a8ce555067446013a88cc31d2d5a77e2e6ea01f5d","first_computed_at":"2026-07-05T00:42:31.696475Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:42:31.696475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+Z2cigRTBx102eqm2RgEffkkhdJW4nHUWZkOVdUay9t79vECNZal/BoUKqKxrAZ2ItvuMpB2xqM9nF/keGPbBw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:42:31.696975Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.04155","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cddb24c3cd112125c695a0d832be60f7302cece3fc11001fc30a9d091e1f548f","sha256:263689665e6cbe553c72c16d70c63688836f35e00d33a8370d08994955099beb"],"state_sha256":"9a24058160e58e2826b147062d22259ae534171918913ebf2c4f8528007fae61"}