{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ZGOKQ5FOOQ5GJ67GUF6VYFTWRL","short_pith_number":"pith:ZGOKQ5FO","schema_version":"1.0","canonical_sha256":"c99ca874ae743a64fbe6a17d5c16768ae52b99821a665db8ab50bfe1eaa16111","source":{"kind":"arxiv","id":"1904.07969","version":1},"attestation_state":"computed","paper":{"title":"DNN Architecture for High Performance Prediction on Natural Videos Loses Submodule's Ability to Learn Discrete-World Dataset","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Atsushi Noda, Lana Sinapayen","submitted_at":"2019-04-16T20:35:09Z","abstract_excerpt":"Is cognition a collection of loosely connected functions tuned to different tasks, or can there be a general learning algorithm? If such an hypothetical general algorithm did exist, tuned to our world, could it adapt seamlessly to a world with different laws of nature? We consider the theory that predictive coding is such a general rule, and falsify it for one specific neural architecture known for high-performance predictions on natural videos and replication of human visual illusions: PredNet. Our results show that PredNet's high performance generalizes without retraining on a completely dif"},"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":"1904.07969","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-16T20:35:09Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"88d00d19867d9e065f282a31e92692a68630b994b491a85e3e46c000e95877d2","abstract_canon_sha256":"2ddde60d139799e82fccc391e59f3fb6efa51465e5a4a4063b8bbab4546f84ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:19.249370Z","signature_b64":"Yq5vShUe/EH8ZjqTgMxRm1wyaPrx5+bG8DG/qHX5WWLXRVOx9OKNp/afUxcZysBZo3aN/SjFXMzl7HbVLANIBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c99ca874ae743a64fbe6a17d5c16768ae52b99821a665db8ab50bfe1eaa16111","last_reissued_at":"2026-05-17T23:48:19.248724Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:19.248724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DNN Architecture for High Performance Prediction on Natural Videos Loses Submodule's Ability to Learn Discrete-World Dataset","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Atsushi Noda, Lana Sinapayen","submitted_at":"2019-04-16T20:35:09Z","abstract_excerpt":"Is cognition a collection of loosely connected functions tuned to different tasks, or can there be a general learning algorithm? If such an hypothetical general algorithm did exist, tuned to our world, could it adapt seamlessly to a world with different laws of nature? We consider the theory that predictive coding is such a general rule, and falsify it for one specific neural architecture known for high-performance predictions on natural videos and replication of human visual illusions: PredNet. Our results show that PredNet's high performance generalizes without retraining on a completely dif"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.07969","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":"1904.07969","created_at":"2026-05-17T23:48:19.248834+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.07969v1","created_at":"2026-05-17T23:48:19.248834+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.07969","created_at":"2026-05-17T23:48:19.248834+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZGOKQ5FOOQ5G","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZGOKQ5FOOQ5GJ67G","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZGOKQ5FO","created_at":"2026-05-18T12:33:33.725879+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/ZGOKQ5FOOQ5GJ67GUF6VYFTWRL","json":"https://pith.science/pith/ZGOKQ5FOOQ5GJ67GUF6VYFTWRL.json","graph_json":"https://pith.science/api/pith-number/ZGOKQ5FOOQ5GJ67GUF6VYFTWRL/graph.json","events_json":"https://pith.science/api/pith-number/ZGOKQ5FOOQ5GJ67GUF6VYFTWRL/events.json","paper":"https://pith.science/paper/ZGOKQ5FO"},"agent_actions":{"view_html":"https://pith.science/pith/ZGOKQ5FOOQ5GJ67GUF6VYFTWRL","download_json":"https://pith.science/pith/ZGOKQ5FOOQ5GJ67GUF6VYFTWRL.json","view_paper":"https://pith.science/paper/ZGOKQ5FO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.07969&json=true","fetch_graph":"https://pith.science/api/pith-number/ZGOKQ5FOOQ5GJ67GUF6VYFTWRL/graph.json","fetch_events":"https://pith.science/api/pith-number/ZGOKQ5FOOQ5GJ67GUF6VYFTWRL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZGOKQ5FOOQ5GJ67GUF6VYFTWRL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZGOKQ5FOOQ5GJ67GUF6VYFTWRL/action/storage_attestation","attest_author":"https://pith.science/pith/ZGOKQ5FOOQ5GJ67GUF6VYFTWRL/action/author_attestation","sign_citation":"https://pith.science/pith/ZGOKQ5FOOQ5GJ67GUF6VYFTWRL/action/citation_signature","submit_replication":"https://pith.science/pith/ZGOKQ5FOOQ5GJ67GUF6VYFTWRL/action/replication_record"}},"created_at":"2026-05-17T23:48:19.248834+00:00","updated_at":"2026-05-17T23:48:19.248834+00:00"}