{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:7WTBXPEHDJAYYCUDKKOUEGZB7U","short_pith_number":"pith:7WTBXPEH","schema_version":"1.0","canonical_sha256":"fda61bbc871a418c0a83529d421b21fd17581a389fc22ae53e98cfa6b976f10b","source":{"kind":"arxiv","id":"1402.2333","version":1},"attestation_state":"computed","paper":{"title":"Modeling sequential data using higher-order relational features and predictive training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Kishore Konda, Roland Memisevic, Vincent Michalski","submitted_at":"2014-02-10T23:53:29Z","abstract_excerpt":"Bi-linear feature learning models, like the gated autoencoder, were proposed as a way to model relationships between frames in a video. By minimizing reconstruction error of one frame, given the previous frame, these models learn \"mapping units\" that encode the transformations inherent in a sequence, and thereby learn to encode motion. In this work we extend bi-linear models by introducing \"higher-order mapping units\" that allow us to encode transformations between frames and transformations between transformations.\n  We show that this makes it possible to encode temporal structure that is mor"},"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":"1402.2333","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-02-10T23:53:29Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"adef18a482c48ea4edac02da545fa4a8520eec1ffce45b940a358b5f5113ebd0","abstract_canon_sha256":"c63fbdfe3412533fb86e89b8a28911f8c1d2f912a60bdf97fc3854d8c9288d37"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:59:31.111058Z","signature_b64":"gO+y1EA/TaxyzMUcqnokRI9se8T2u9OeWNUmpyXczrYCgeaaAAe2P26CtxAq+kbjCmKySdA6zP1Y5XFxcMFQBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fda61bbc871a418c0a83529d421b21fd17581a389fc22ae53e98cfa6b976f10b","last_reissued_at":"2026-05-18T02:59:31.110258Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:59:31.110258Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modeling sequential data using higher-order relational features and predictive training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Kishore Konda, Roland Memisevic, Vincent Michalski","submitted_at":"2014-02-10T23:53:29Z","abstract_excerpt":"Bi-linear feature learning models, like the gated autoencoder, were proposed as a way to model relationships between frames in a video. By minimizing reconstruction error of one frame, given the previous frame, these models learn \"mapping units\" that encode the transformations inherent in a sequence, and thereby learn to encode motion. In this work we extend bi-linear models by introducing \"higher-order mapping units\" that allow us to encode transformations between frames and transformations between transformations.\n  We show that this makes it possible to encode temporal structure that is mor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.2333","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":"1402.2333","created_at":"2026-05-18T02:59:31.110386+00:00"},{"alias_kind":"arxiv_version","alias_value":"1402.2333v1","created_at":"2026-05-18T02:59:31.110386+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.2333","created_at":"2026-05-18T02:59:31.110386+00:00"},{"alias_kind":"pith_short_12","alias_value":"7WTBXPEHDJAY","created_at":"2026-05-18T12:28:19.803747+00:00"},{"alias_kind":"pith_short_16","alias_value":"7WTBXPEHDJAYYCUD","created_at":"2026-05-18T12:28:19.803747+00:00"},{"alias_kind":"pith_short_8","alias_value":"7WTBXPEH","created_at":"2026-05-18T12:28:19.803747+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/7WTBXPEHDJAYYCUDKKOUEGZB7U","json":"https://pith.science/pith/7WTBXPEHDJAYYCUDKKOUEGZB7U.json","graph_json":"https://pith.science/api/pith-number/7WTBXPEHDJAYYCUDKKOUEGZB7U/graph.json","events_json":"https://pith.science/api/pith-number/7WTBXPEHDJAYYCUDKKOUEGZB7U/events.json","paper":"https://pith.science/paper/7WTBXPEH"},"agent_actions":{"view_html":"https://pith.science/pith/7WTBXPEHDJAYYCUDKKOUEGZB7U","download_json":"https://pith.science/pith/7WTBXPEHDJAYYCUDKKOUEGZB7U.json","view_paper":"https://pith.science/paper/7WTBXPEH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1402.2333&json=true","fetch_graph":"https://pith.science/api/pith-number/7WTBXPEHDJAYYCUDKKOUEGZB7U/graph.json","fetch_events":"https://pith.science/api/pith-number/7WTBXPEHDJAYYCUDKKOUEGZB7U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7WTBXPEHDJAYYCUDKKOUEGZB7U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7WTBXPEHDJAYYCUDKKOUEGZB7U/action/storage_attestation","attest_author":"https://pith.science/pith/7WTBXPEHDJAYYCUDKKOUEGZB7U/action/author_attestation","sign_citation":"https://pith.science/pith/7WTBXPEHDJAYYCUDKKOUEGZB7U/action/citation_signature","submit_replication":"https://pith.science/pith/7WTBXPEHDJAYYCUDKKOUEGZB7U/action/replication_record"}},"created_at":"2026-05-18T02:59:31.110386+00:00","updated_at":"2026-05-18T02:59:31.110386+00:00"}