{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:BHFYYWZ4IF2ZKSJJVL7SJVMS3W","short_pith_number":"pith:BHFYYWZ4","schema_version":"1.0","canonical_sha256":"09cb8c5b3c4175954929aaff24d592dda4b39c68d85e51a39547103d8293ba3e","source":{"kind":"arxiv","id":"1709.08520","version":1},"attestation_state":"computed","paper":{"title":"Predictive-State Decoders: Encoding the Future into Recurrent Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Arun Venkatraman, Byron Boots, J. Andrew Bagnell, Kris M. Kitani, Lerrel Pinto, Martial Hebert, Nicholas Rhinehart, Wen Sun","submitted_at":"2017-09-25T14:40:18Z","abstract_excerpt":"Recurrent neural networks (RNNs) are a vital modeling technique that rely on internal states learned indirectly by optimization of a supervised, unsupervised, or reinforcement training loss. RNNs are used to model dynamic processes that are characterized by underlying latent states whose form is often unknown, precluding its analytic representation inside an RNN. In the Predictive-State Representation (PSR) literature, latent state processes are modeled by an internal state representation that directly models the distribution of future observations, and most recent work in this area has relied"},"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":"1709.08520","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-25T14:40:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0c009f098e1dbc940318891264fb2b9b863bb04a003dd50615e7cb23ef83041e","abstract_canon_sha256":"ac90f457058c313e18a519461f2c7964e13c7e1b1bf792cef91f1eecd3ff8fc8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:25.418335Z","signature_b64":"W0xb3c+Q3SCv1x40Tb+5KNbM4OvPTNbbZ/hfmBCBvFjAgVHAg6WFgPlyqvz0HUpofAVBDCc/+NjNBnm0w0HYDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"09cb8c5b3c4175954929aaff24d592dda4b39c68d85e51a39547103d8293ba3e","last_reissued_at":"2026-05-18T00:34:25.417877Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:25.417877Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Predictive-State Decoders: Encoding the Future into Recurrent Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Arun Venkatraman, Byron Boots, J. Andrew Bagnell, Kris M. Kitani, Lerrel Pinto, Martial Hebert, Nicholas Rhinehart, Wen Sun","submitted_at":"2017-09-25T14:40:18Z","abstract_excerpt":"Recurrent neural networks (RNNs) are a vital modeling technique that rely on internal states learned indirectly by optimization of a supervised, unsupervised, or reinforcement training loss. RNNs are used to model dynamic processes that are characterized by underlying latent states whose form is often unknown, precluding its analytic representation inside an RNN. In the Predictive-State Representation (PSR) literature, latent state processes are modeled by an internal state representation that directly models the distribution of future observations, and most recent work in this area has relied"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.08520","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":"1709.08520","created_at":"2026-05-18T00:34:25.417939+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.08520v1","created_at":"2026-05-18T00:34:25.417939+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.08520","created_at":"2026-05-18T00:34:25.417939+00:00"},{"alias_kind":"pith_short_12","alias_value":"BHFYYWZ4IF2Z","created_at":"2026-05-18T12:31:08.081275+00:00"},{"alias_kind":"pith_short_16","alias_value":"BHFYYWZ4IF2ZKSJJ","created_at":"2026-05-18T12:31:08.081275+00:00"},{"alias_kind":"pith_short_8","alias_value":"BHFYYWZ4","created_at":"2026-05-18T12:31:08.081275+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/BHFYYWZ4IF2ZKSJJVL7SJVMS3W","json":"https://pith.science/pith/BHFYYWZ4IF2ZKSJJVL7SJVMS3W.json","graph_json":"https://pith.science/api/pith-number/BHFYYWZ4IF2ZKSJJVL7SJVMS3W/graph.json","events_json":"https://pith.science/api/pith-number/BHFYYWZ4IF2ZKSJJVL7SJVMS3W/events.json","paper":"https://pith.science/paper/BHFYYWZ4"},"agent_actions":{"view_html":"https://pith.science/pith/BHFYYWZ4IF2ZKSJJVL7SJVMS3W","download_json":"https://pith.science/pith/BHFYYWZ4IF2ZKSJJVL7SJVMS3W.json","view_paper":"https://pith.science/paper/BHFYYWZ4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.08520&json=true","fetch_graph":"https://pith.science/api/pith-number/BHFYYWZ4IF2ZKSJJVL7SJVMS3W/graph.json","fetch_events":"https://pith.science/api/pith-number/BHFYYWZ4IF2ZKSJJVL7SJVMS3W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BHFYYWZ4IF2ZKSJJVL7SJVMS3W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BHFYYWZ4IF2ZKSJJVL7SJVMS3W/action/storage_attestation","attest_author":"https://pith.science/pith/BHFYYWZ4IF2ZKSJJVL7SJVMS3W/action/author_attestation","sign_citation":"https://pith.science/pith/BHFYYWZ4IF2ZKSJJVL7SJVMS3W/action/citation_signature","submit_replication":"https://pith.science/pith/BHFYYWZ4IF2ZKSJJVL7SJVMS3W/action/replication_record"}},"created_at":"2026-05-18T00:34:25.417939+00:00","updated_at":"2026-05-18T00:34:25.417939+00:00"}