{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:U34MQPL3KSWHZZZSMU6JBIODQS","short_pith_number":"pith:U34MQPL3","schema_version":"1.0","canonical_sha256":"a6f8c83d7b54ac7ce732653c90a1c3848a88affd115291c7227efe73ea3a4622","source":{"kind":"arxiv","id":"1902.02405","version":1},"attestation_state":"computed","paper":{"title":"On the Variance of Unbiased Online Recurrent Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"James Martens, Tim Cooijmans","submitted_at":"2019-02-06T21:46:34Z","abstract_excerpt":"The recently proposed Unbiased Online Recurrent Optimization algorithm (UORO, arXiv:1702.05043) uses an unbiased approximation of RTRL to achieve fully online gradient-based learning in RNNs. In this work we analyze the variance of the gradient estimate computed by UORO, and propose several possible changes to the method which reduce this variance both in theory and practice. We also contribute significantly to the theoretical and intuitive understanding of UORO (and its existing variance reduction technique), and demonstrate a fundamental connection between its gradient estimate and the one t"},"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":"1902.02405","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-06T21:46:34Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"30cb1c090f0cc1614ab346fbfb600c3658f5fab08fb7fcabd77dfa621f858087","abstract_canon_sha256":"13cd27f3a3901394c3b8f6d381b1eb3925e45898e62e39c4e64aa63d5065036c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:33.461729Z","signature_b64":"DtYxvObjyvDz0/iy8UFM36ut29tLoC3hHB6SbKbSz6P6fbu2UtD2ggK23Hl7wEoI6qmoy02xLBrwo8c+9XNFCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6f8c83d7b54ac7ce732653c90a1c3848a88affd115291c7227efe73ea3a4622","last_reissued_at":"2026-05-17T23:54:33.460965Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:33.460965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Variance of Unbiased Online Recurrent Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"James Martens, Tim Cooijmans","submitted_at":"2019-02-06T21:46:34Z","abstract_excerpt":"The recently proposed Unbiased Online Recurrent Optimization algorithm (UORO, arXiv:1702.05043) uses an unbiased approximation of RTRL to achieve fully online gradient-based learning in RNNs. In this work we analyze the variance of the gradient estimate computed by UORO, and propose several possible changes to the method which reduce this variance both in theory and practice. We also contribute significantly to the theoretical and intuitive understanding of UORO (and its existing variance reduction technique), and demonstrate a fundamental connection between its gradient estimate and the one t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.02405","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":"1902.02405","created_at":"2026-05-17T23:54:33.461089+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.02405v1","created_at":"2026-05-17T23:54:33.461089+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.02405","created_at":"2026-05-17T23:54:33.461089+00:00"},{"alias_kind":"pith_short_12","alias_value":"U34MQPL3KSWH","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"U34MQPL3KSWHZZZS","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"U34MQPL3","created_at":"2026-05-18T12:33:30.264802+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.02649","citing_title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U34MQPL3KSWHZZZSMU6JBIODQS","json":"https://pith.science/pith/U34MQPL3KSWHZZZSMU6JBIODQS.json","graph_json":"https://pith.science/api/pith-number/U34MQPL3KSWHZZZSMU6JBIODQS/graph.json","events_json":"https://pith.science/api/pith-number/U34MQPL3KSWHZZZSMU6JBIODQS/events.json","paper":"https://pith.science/paper/U34MQPL3"},"agent_actions":{"view_html":"https://pith.science/pith/U34MQPL3KSWHZZZSMU6JBIODQS","download_json":"https://pith.science/pith/U34MQPL3KSWHZZZSMU6JBIODQS.json","view_paper":"https://pith.science/paper/U34MQPL3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.02405&json=true","fetch_graph":"https://pith.science/api/pith-number/U34MQPL3KSWHZZZSMU6JBIODQS/graph.json","fetch_events":"https://pith.science/api/pith-number/U34MQPL3KSWHZZZSMU6JBIODQS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U34MQPL3KSWHZZZSMU6JBIODQS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U34MQPL3KSWHZZZSMU6JBIODQS/action/storage_attestation","attest_author":"https://pith.science/pith/U34MQPL3KSWHZZZSMU6JBIODQS/action/author_attestation","sign_citation":"https://pith.science/pith/U34MQPL3KSWHZZZSMU6JBIODQS/action/citation_signature","submit_replication":"https://pith.science/pith/U34MQPL3KSWHZZZSMU6JBIODQS/action/replication_record"}},"created_at":"2026-05-17T23:54:33.461089+00:00","updated_at":"2026-05-17T23:54:33.461089+00:00"}