{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:RS7WCK4ZVYLKMDHQFSJOX2PRBP","short_pith_number":"pith:RS7WCK4Z","schema_version":"1.0","canonical_sha256":"8cbf612b99ae16a60cf02c92ebe9f10bd84ffa6fd2bb54de0353f87b29ea8f35","source":{"kind":"arxiv","id":"1602.07109","version":5},"attestation_state":"computed","paper":{"title":"Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Justin Bayer, Marvin Ludersdorfer, Maximilian Soelch, Patrick van der Smagt","submitted_at":"2016-02-23T10:31:51Z","abstract_excerpt":"Approximate variational inference has shown to be a powerful tool for modeling unknown complex probability distributions. Recent advances in the field allow us to learn probabilistic models of sequences that actively exploit spatial and temporal structure. We apply a Stochastic Recurrent Network (STORN) to learn robot time series data. Our evaluation demonstrates that we can robustly detect anomalies both off- and on-line."},"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":"1602.07109","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-23T10:31:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"00490beefdf394e1d1f96f7ae799241eec77bf1154e3489720c0e768b80dab68","abstract_canon_sha256":"693a68b682e6c539b94d3778df35b948660e1acd0066e71d3c883aa278125d57"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:29.964649Z","signature_b64":"8dn4INdW6HUGrC6jCBCL0T+kAL2dezsCqaGEKOdyNm+FTvq3YVnIPa20ujyhlh/bt6VtZzRu4vD14kc2eLBMCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8cbf612b99ae16a60cf02c92ebe9f10bd84ffa6fd2bb54de0353f87b29ea8f35","last_reissued_at":"2026-05-18T01:12:29.964307Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:29.964307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Justin Bayer, Marvin Ludersdorfer, Maximilian Soelch, Patrick van der Smagt","submitted_at":"2016-02-23T10:31:51Z","abstract_excerpt":"Approximate variational inference has shown to be a powerful tool for modeling unknown complex probability distributions. Recent advances in the field allow us to learn probabilistic models of sequences that actively exploit spatial and temporal structure. We apply a Stochastic Recurrent Network (STORN) to learn robot time series data. Our evaluation demonstrates that we can robustly detect anomalies both off- and on-line."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.07109","kind":"arxiv","version":5},"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":"1602.07109","created_at":"2026-05-18T01:12:29.964363+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.07109v5","created_at":"2026-05-18T01:12:29.964363+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.07109","created_at":"2026-05-18T01:12:29.964363+00:00"},{"alias_kind":"pith_short_12","alias_value":"RS7WCK4ZVYLK","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_16","alias_value":"RS7WCK4ZVYLKMDHQ","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_8","alias_value":"RS7WCK4Z","created_at":"2026-05-18T12:30:41.710351+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/RS7WCK4ZVYLKMDHQFSJOX2PRBP","json":"https://pith.science/pith/RS7WCK4ZVYLKMDHQFSJOX2PRBP.json","graph_json":"https://pith.science/api/pith-number/RS7WCK4ZVYLKMDHQFSJOX2PRBP/graph.json","events_json":"https://pith.science/api/pith-number/RS7WCK4ZVYLKMDHQFSJOX2PRBP/events.json","paper":"https://pith.science/paper/RS7WCK4Z"},"agent_actions":{"view_html":"https://pith.science/pith/RS7WCK4ZVYLKMDHQFSJOX2PRBP","download_json":"https://pith.science/pith/RS7WCK4ZVYLKMDHQFSJOX2PRBP.json","view_paper":"https://pith.science/paper/RS7WCK4Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.07109&json=true","fetch_graph":"https://pith.science/api/pith-number/RS7WCK4ZVYLKMDHQFSJOX2PRBP/graph.json","fetch_events":"https://pith.science/api/pith-number/RS7WCK4ZVYLKMDHQFSJOX2PRBP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RS7WCK4ZVYLKMDHQFSJOX2PRBP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RS7WCK4ZVYLKMDHQFSJOX2PRBP/action/storage_attestation","attest_author":"https://pith.science/pith/RS7WCK4ZVYLKMDHQFSJOX2PRBP/action/author_attestation","sign_citation":"https://pith.science/pith/RS7WCK4ZVYLKMDHQFSJOX2PRBP/action/citation_signature","submit_replication":"https://pith.science/pith/RS7WCK4ZVYLKMDHQFSJOX2PRBP/action/replication_record"}},"created_at":"2026-05-18T01:12:29.964363+00:00","updated_at":"2026-05-18T01:12:29.964363+00:00"}