{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:JVS3LXULNMAGAPWPRRH7JWN4WP","short_pith_number":"pith:JVS3LXUL","schema_version":"1.0","canonical_sha256":"4d65b5de8b6b00603ecf8c4ff4d9bcb3c18a59ccfd1581a6caddd6bf3a0df1d3","source":{"kind":"arxiv","id":"1811.05933","version":1},"attestation_state":"computed","paper":{"title":"Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"eess.SP","authors_text":"Arash Mehrjou, Bernhard Sch\\\"olkopf","submitted_at":"2018-11-14T18:02:58Z","abstract_excerpt":"Filtering is a general name for inferring the states of a dynamical system given observations. The most common filtering approach is Gaussian Filtering (GF) where the distribution of the inferred states is a Gaussian whose mean is an affine function of the observations. There are two restrictions in this model: Gaussianity and Affinity. We propose a model to relax both these assumptions based on recent advances in implicit generative models. Empirical results show that the proposed method gives a significant advantage over GF and nonlinear methods based on fixed nonlinear kernels."},"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":"1811.05933","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-11-14T18:02:58Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"e32eb3d3b2ec036eece9ede6f386b92fe565fc8f70efe7d1e7a798368b4efb47","abstract_canon_sha256":"885c80b63a5d49a84eeb3f4c2d6047ab3b065e0e63f5cb3a54a5af232b7506aa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:17.528826Z","signature_b64":"eLAj0uuGpXRFHvKagcCfX0/wVtUHOhA+uJ1TljXtMLqSjvmebsO79PADQNRw/0vVwtDWhPuprXpohUSmQQVQBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d65b5de8b6b00603ecf8c4ff4d9bcb3c18a59ccfd1581a6caddd6bf3a0df1d3","last_reissued_at":"2026-05-18T00:00:17.528264Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:17.528264Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"eess.SP","authors_text":"Arash Mehrjou, Bernhard Sch\\\"olkopf","submitted_at":"2018-11-14T18:02:58Z","abstract_excerpt":"Filtering is a general name for inferring the states of a dynamical system given observations. The most common filtering approach is Gaussian Filtering (GF) where the distribution of the inferred states is a Gaussian whose mean is an affine function of the observations. There are two restrictions in this model: Gaussianity and Affinity. We propose a model to relax both these assumptions based on recent advances in implicit generative models. Empirical results show that the proposed method gives a significant advantage over GF and nonlinear methods based on fixed nonlinear kernels."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.05933","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":""},"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":"1811.05933","created_at":"2026-05-18T00:00:17.528365+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.05933v1","created_at":"2026-05-18T00:00:17.528365+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.05933","created_at":"2026-05-18T00:00:17.528365+00:00"},{"alias_kind":"pith_short_12","alias_value":"JVS3LXULNMAG","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_16","alias_value":"JVS3LXULNMAGAPWP","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_8","alias_value":"JVS3LXUL","created_at":"2026-05-18T12:32:33.847187+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/JVS3LXULNMAGAPWPRRH7JWN4WP","json":"https://pith.science/pith/JVS3LXULNMAGAPWPRRH7JWN4WP.json","graph_json":"https://pith.science/api/pith-number/JVS3LXULNMAGAPWPRRH7JWN4WP/graph.json","events_json":"https://pith.science/api/pith-number/JVS3LXULNMAGAPWPRRH7JWN4WP/events.json","paper":"https://pith.science/paper/JVS3LXUL"},"agent_actions":{"view_html":"https://pith.science/pith/JVS3LXULNMAGAPWPRRH7JWN4WP","download_json":"https://pith.science/pith/JVS3LXULNMAGAPWPRRH7JWN4WP.json","view_paper":"https://pith.science/paper/JVS3LXUL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.05933&json=true","fetch_graph":"https://pith.science/api/pith-number/JVS3LXULNMAGAPWPRRH7JWN4WP/graph.json","fetch_events":"https://pith.science/api/pith-number/JVS3LXULNMAGAPWPRRH7JWN4WP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JVS3LXULNMAGAPWPRRH7JWN4WP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JVS3LXULNMAGAPWPRRH7JWN4WP/action/storage_attestation","attest_author":"https://pith.science/pith/JVS3LXULNMAGAPWPRRH7JWN4WP/action/author_attestation","sign_citation":"https://pith.science/pith/JVS3LXULNMAGAPWPRRH7JWN4WP/action/citation_signature","submit_replication":"https://pith.science/pith/JVS3LXULNMAGAPWPRRH7JWN4WP/action/replication_record"}},"created_at":"2026-05-18T00:00:17.528365+00:00","updated_at":"2026-05-18T00:00:17.528365+00:00"}