{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JU22MCEUOD7NL2FCOHJF452TSN","short_pith_number":"pith:JU22MCEU","schema_version":"1.0","canonical_sha256":"4d35a6089470fed5e8a271d25e7753937d7f11766004fbc75cfbe399c0b7839f","source":{"kind":"arxiv","id":"2304.07827","version":2},"attestation_state":"computed","paper":{"title":"Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Damiano Steger, Guy Revach, Itay Buchnik, Nir Shlezinger, Ruud J. G. van Sloun, Tirza Routtenberg","submitted_at":"2023-04-16T16:36:21Z","abstract_excerpt":"The Kalman filter (KF) is a widely-used algorithm for tracking dynamic systems that are captured by state space (SS) models. The need to fully describe a SS model limits its applicability under complex settings, e.g., when tracking based on visual data, and the processing of high-dimensional signals often induces notable latency. These challenges can be treated by mapping the measurements into latent features obeying some postulated closed-form SS model, and applying the KF in the latent space. However, the validity of this approximated SS model may constitute a limiting factor. In this work, "},"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":"2304.07827","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2023-04-16T16:36:21Z","cross_cats_sorted":[],"title_canon_sha256":"7545682c28e1715224dba0995f38dec012c39952c71a8425da12bafd4673dcc1","abstract_canon_sha256":"88edfbf3bc777acab731030cf9115df731d8cda7be0553b35dcd894a82d9c707"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:02:50.697601Z","signature_b64":"dwLN0Kep3sWQ3dezJ7Lu1SIkYbbQxnFrzGnQLmv8F80+gQkLmLYpgzFUYpUEzdjgeG7M9FoQD6J604ev91AHAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d35a6089470fed5e8a271d25e7753937d7f11766004fbc75cfbe399c0b7839f","last_reissued_at":"2026-07-05T06:02:50.697116Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:02:50.697116Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Damiano Steger, Guy Revach, Itay Buchnik, Nir Shlezinger, Ruud J. G. van Sloun, Tirza Routtenberg","submitted_at":"2023-04-16T16:36:21Z","abstract_excerpt":"The Kalman filter (KF) is a widely-used algorithm for tracking dynamic systems that are captured by state space (SS) models. The need to fully describe a SS model limits its applicability under complex settings, e.g., when tracking based on visual data, and the processing of high-dimensional signals often induces notable latency. These challenges can be treated by mapping the measurements into latent features obeying some postulated closed-form SS model, and applying the KF in the latent space. However, the validity of this approximated SS model may constitute a limiting factor. In this work, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.07827","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/2304.07827/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":"2304.07827","created_at":"2026-07-05T06:02:50.697180+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.07827v2","created_at":"2026-07-05T06:02:50.697180+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.07827","created_at":"2026-07-05T06:02:50.697180+00:00"},{"alias_kind":"pith_short_12","alias_value":"JU22MCEUOD7N","created_at":"2026-07-05T06:02:50.697180+00:00"},{"alias_kind":"pith_short_16","alias_value":"JU22MCEUOD7NL2FC","created_at":"2026-07-05T06:02:50.697180+00:00"},{"alias_kind":"pith_short_8","alias_value":"JU22MCEU","created_at":"2026-07-05T06:02:50.697180+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JU22MCEUOD7NL2FCOHJF452TSN","json":"https://pith.science/pith/JU22MCEUOD7NL2FCOHJF452TSN.json","graph_json":"https://pith.science/api/pith-number/JU22MCEUOD7NL2FCOHJF452TSN/graph.json","events_json":"https://pith.science/api/pith-number/JU22MCEUOD7NL2FCOHJF452TSN/events.json","paper":"https://pith.science/paper/JU22MCEU"},"agent_actions":{"view_html":"https://pith.science/pith/JU22MCEUOD7NL2FCOHJF452TSN","download_json":"https://pith.science/pith/JU22MCEUOD7NL2FCOHJF452TSN.json","view_paper":"https://pith.science/paper/JU22MCEU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.07827&json=true","fetch_graph":"https://pith.science/api/pith-number/JU22MCEUOD7NL2FCOHJF452TSN/graph.json","fetch_events":"https://pith.science/api/pith-number/JU22MCEUOD7NL2FCOHJF452TSN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JU22MCEUOD7NL2FCOHJF452TSN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JU22MCEUOD7NL2FCOHJF452TSN/action/storage_attestation","attest_author":"https://pith.science/pith/JU22MCEUOD7NL2FCOHJF452TSN/action/author_attestation","sign_citation":"https://pith.science/pith/JU22MCEUOD7NL2FCOHJF452TSN/action/citation_signature","submit_replication":"https://pith.science/pith/JU22MCEUOD7NL2FCOHJF452TSN/action/replication_record"}},"created_at":"2026-07-05T06:02:50.697180+00:00","updated_at":"2026-07-05T06:02:50.697180+00:00"}