{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:227L2GL2ZN2MLJFGT4Z2OODJRC","short_pith_number":"pith:227L2GL2","schema_version":"1.0","canonical_sha256":"d6bebd197acb74c5a4a69f33a7386988b3f4050fe886a44516ea9c5b9ee4b61f","source":{"kind":"arxiv","id":"1704.02848","version":2},"attestation_state":"computed","paper":{"title":"Unsupervised prototype learning in an associative-memory network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","cs.LG"],"primary_cat":"cs.NE","authors_text":"Hai-Jun Zhou, Huiling Zhen, Shang-Nan Wang","submitted_at":"2017-04-10T13:20:23Z","abstract_excerpt":"Unsupervised learning in a generalized Hopfield associative-memory network is investigated in this work. First, we prove that the (generalized) Hopfield model is equivalent to a semi-restricted Boltzmann machine with a layer of visible neurons and another layer of hidden binary neurons, so it could serve as the building block for a multilayered deep-learning system. We then demonstrate that the Hopfield network can learn to form a faithful internal representation of the observed samples, with the learned memory patterns being prototypes of the input data. Furthermore, we propose a spectral met"},"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":"1704.02848","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-04-10T13:20:23Z","cross_cats_sorted":["cond-mat.dis-nn","cs.LG"],"title_canon_sha256":"37fc344c31d444f9cbc283b7b5f9f8efc8e007a2f4fe5be0c73d138b6b3f7e7a","abstract_canon_sha256":"75aaee82b84393775667a6255bc74c136a732e695bc0b3573125d3f30b6cbc92"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:35.945126Z","signature_b64":"oO+umGF3jZaDh9AW/u9DxADh5jS73tg366YQTyz8DiGy7WgACinbFR2m0wv3ERExBbLbfd951qiiILCEyDI9Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6bebd197acb74c5a4a69f33a7386988b3f4050fe886a44516ea9c5b9ee4b61f","last_reissued_at":"2026-05-18T00:39:35.944530Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:35.944530Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised prototype learning in an associative-memory network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","cs.LG"],"primary_cat":"cs.NE","authors_text":"Hai-Jun Zhou, Huiling Zhen, Shang-Nan Wang","submitted_at":"2017-04-10T13:20:23Z","abstract_excerpt":"Unsupervised learning in a generalized Hopfield associative-memory network is investigated in this work. First, we prove that the (generalized) Hopfield model is equivalent to a semi-restricted Boltzmann machine with a layer of visible neurons and another layer of hidden binary neurons, so it could serve as the building block for a multilayered deep-learning system. We then demonstrate that the Hopfield network can learn to form a faithful internal representation of the observed samples, with the learned memory patterns being prototypes of the input data. Furthermore, we propose a spectral met"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.02848","kind":"arxiv","version":2},"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":"1704.02848","created_at":"2026-05-18T00:39:35.944627+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.02848v2","created_at":"2026-05-18T00:39:35.944627+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.02848","created_at":"2026-05-18T00:39:35.944627+00:00"},{"alias_kind":"pith_short_12","alias_value":"227L2GL2ZN2M","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_16","alias_value":"227L2GL2ZN2MLJFG","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_8","alias_value":"227L2GL2","created_at":"2026-05-18T12:30:55.937587+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/227L2GL2ZN2MLJFGT4Z2OODJRC","json":"https://pith.science/pith/227L2GL2ZN2MLJFGT4Z2OODJRC.json","graph_json":"https://pith.science/api/pith-number/227L2GL2ZN2MLJFGT4Z2OODJRC/graph.json","events_json":"https://pith.science/api/pith-number/227L2GL2ZN2MLJFGT4Z2OODJRC/events.json","paper":"https://pith.science/paper/227L2GL2"},"agent_actions":{"view_html":"https://pith.science/pith/227L2GL2ZN2MLJFGT4Z2OODJRC","download_json":"https://pith.science/pith/227L2GL2ZN2MLJFGT4Z2OODJRC.json","view_paper":"https://pith.science/paper/227L2GL2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.02848&json=true","fetch_graph":"https://pith.science/api/pith-number/227L2GL2ZN2MLJFGT4Z2OODJRC/graph.json","fetch_events":"https://pith.science/api/pith-number/227L2GL2ZN2MLJFGT4Z2OODJRC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/227L2GL2ZN2MLJFGT4Z2OODJRC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/227L2GL2ZN2MLJFGT4Z2OODJRC/action/storage_attestation","attest_author":"https://pith.science/pith/227L2GL2ZN2MLJFGT4Z2OODJRC/action/author_attestation","sign_citation":"https://pith.science/pith/227L2GL2ZN2MLJFGT4Z2OODJRC/action/citation_signature","submit_replication":"https://pith.science/pith/227L2GL2ZN2MLJFGT4Z2OODJRC/action/replication_record"}},"created_at":"2026-05-18T00:39:35.944627+00:00","updated_at":"2026-05-18T00:39:35.944627+00:00"}