{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FS5YWYI343VZ5YJK3BA2LYJA3S","short_pith_number":"pith:FS5YWYI3","schema_version":"1.0","canonical_sha256":"2cbb8b611be6eb9ee12ad841a5e120dcb2661dcf866465dbe896ef05fa55a8d0","source":{"kind":"arxiv","id":"2309.03058","version":3},"attestation_state":"computed","paper":{"title":"Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Guy Revach, Jindrich Dunik, Nir Shlezinger, Yehonatan Dahan","submitted_at":"2023-09-06T14:59:26Z","abstract_excerpt":"Recent years have witnessed a growing interest in tracking algorithms that augment Kalman Filters (KFs) with Deep Neural Networks (DNNs). By transforming KFs into trainable deep learning models, one can learn from data to reliably track a latent state in complex and partially known dynamics. However, unlike classic KFs, conventional DNN-based systems do not naturally provide an uncertainty measure, such as error covariance, alongside their estimates, which is crucial in various applications that rely on KF-type tracking. This work bridges this gap by studying error covariance extraction in DNN"},"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":"2309.03058","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2023-09-06T14:59:26Z","cross_cats_sorted":[],"title_canon_sha256":"22b9e6a85b0a5e9b6cc566edb6f9e8e86a7fc30fa0feb2d3a249ff2fb7744e8f","abstract_canon_sha256":"eb5314452fc55911654ccc62b2ed52c7dd9e4e3abe76e1ee28a72e8ffa08d5a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:23:14.239891Z","signature_b64":"sHdp/CRiuS2dCBIRb8LNvDQ8HMcc6MePQ/FpV4NWFarngWXJ7oqVJIfJ0XPoE/PCdLE3n4zzDJlv+HMMeC7ZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cbb8b611be6eb9ee12ad841a5e120dcb2661dcf866465dbe896ef05fa55a8d0","last_reissued_at":"2026-07-05T11:23:14.239390Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:23:14.239390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Guy Revach, Jindrich Dunik, Nir Shlezinger, Yehonatan Dahan","submitted_at":"2023-09-06T14:59:26Z","abstract_excerpt":"Recent years have witnessed a growing interest in tracking algorithms that augment Kalman Filters (KFs) with Deep Neural Networks (DNNs). By transforming KFs into trainable deep learning models, one can learn from data to reliably track a latent state in complex and partially known dynamics. However, unlike classic KFs, conventional DNN-based systems do not naturally provide an uncertainty measure, such as error covariance, alongside their estimates, which is crucial in various applications that rely on KF-type tracking. This work bridges this gap by studying error covariance extraction in DNN"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.03058","kind":"arxiv","version":3},"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/2309.03058/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":"2309.03058","created_at":"2026-07-05T11:23:14.239449+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.03058v3","created_at":"2026-07-05T11:23:14.239449+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.03058","created_at":"2026-07-05T11:23:14.239449+00:00"},{"alias_kind":"pith_short_12","alias_value":"FS5YWYI343VZ","created_at":"2026-07-05T11:23:14.239449+00:00"},{"alias_kind":"pith_short_16","alias_value":"FS5YWYI343VZ5YJK","created_at":"2026-07-05T11:23:14.239449+00:00"},{"alias_kind":"pith_short_8","alias_value":"FS5YWYI3","created_at":"2026-07-05T11:23:14.239449+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/FS5YWYI343VZ5YJK3BA2LYJA3S","json":"https://pith.science/pith/FS5YWYI343VZ5YJK3BA2LYJA3S.json","graph_json":"https://pith.science/api/pith-number/FS5YWYI343VZ5YJK3BA2LYJA3S/graph.json","events_json":"https://pith.science/api/pith-number/FS5YWYI343VZ5YJK3BA2LYJA3S/events.json","paper":"https://pith.science/paper/FS5YWYI3"},"agent_actions":{"view_html":"https://pith.science/pith/FS5YWYI343VZ5YJK3BA2LYJA3S","download_json":"https://pith.science/pith/FS5YWYI343VZ5YJK3BA2LYJA3S.json","view_paper":"https://pith.science/paper/FS5YWYI3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.03058&json=true","fetch_graph":"https://pith.science/api/pith-number/FS5YWYI343VZ5YJK3BA2LYJA3S/graph.json","fetch_events":"https://pith.science/api/pith-number/FS5YWYI343VZ5YJK3BA2LYJA3S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FS5YWYI343VZ5YJK3BA2LYJA3S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FS5YWYI343VZ5YJK3BA2LYJA3S/action/storage_attestation","attest_author":"https://pith.science/pith/FS5YWYI343VZ5YJK3BA2LYJA3S/action/author_attestation","sign_citation":"https://pith.science/pith/FS5YWYI343VZ5YJK3BA2LYJA3S/action/citation_signature","submit_replication":"https://pith.science/pith/FS5YWYI343VZ5YJK3BA2LYJA3S/action/replication_record"}},"created_at":"2026-07-05T11:23:14.239449+00:00","updated_at":"2026-07-05T11:23:14.239449+00:00"}