{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:L7MPRFEFQNHQLKPUK2JCJEDZWP","short_pith_number":"pith:L7MPRFEF","schema_version":"1.0","canonical_sha256":"5fd8f89485834f05a9f45692249079b3d0a3d98c235e8351766244a7cc7dc8cf","source":{"kind":"arxiv","id":"1805.08952","version":1},"attestation_state":"computed","paper":{"title":"Dictionary Learning by Dynamical Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ping Tak Peter Tang, Tsung-han Lin","submitted_at":"2018-05-23T03:51:21Z","abstract_excerpt":"A dynamical neural network consists of a set of interconnected neurons that interact over time continuously. It can exhibit computational properties in the sense that the dynamical system's evolution and/or limit points in the associated state space can correspond to numerical solutions to certain mathematical optimization or learning problems. Such a computational system is particularly attractive in that it can be mapped to a massively parallel computer architecture for power and throughput efficiency, especially if each neuron can rely solely on local information (i.e., local memory). Deriv"},"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":"1805.08952","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-23T03:51:21Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"aae2810a514137754a9261ddcb4dcaa175141c771e341cf3f3087fa45fec638a","abstract_canon_sha256":"9eaa0fad724b962588a665e1bc707cab70caf0b4dc6f4a12bff6e1e456d7ad2a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:08.974737Z","signature_b64":"+2LI5jZNFtqz5kQKocAyirSbW2eTPMwUwAJqyM6hXfVwmH+LDA9OswnDIU5n4Ond+dV3HYkLDRdtzcnMQd4fAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fd8f89485834f05a9f45692249079b3d0a3d98c235e8351766244a7cc7dc8cf","last_reissued_at":"2026-05-18T00:15:08.974125Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:08.974125Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dictionary Learning by Dynamical Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ping Tak Peter Tang, Tsung-han Lin","submitted_at":"2018-05-23T03:51:21Z","abstract_excerpt":"A dynamical neural network consists of a set of interconnected neurons that interact over time continuously. It can exhibit computational properties in the sense that the dynamical system's evolution and/or limit points in the associated state space can correspond to numerical solutions to certain mathematical optimization or learning problems. Such a computational system is particularly attractive in that it can be mapped to a massively parallel computer architecture for power and throughput efficiency, especially if each neuron can rely solely on local information (i.e., local memory). Deriv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08952","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":"1805.08952","created_at":"2026-05-18T00:15:08.974223+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.08952v1","created_at":"2026-05-18T00:15:08.974223+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08952","created_at":"2026-05-18T00:15:08.974223+00:00"},{"alias_kind":"pith_short_12","alias_value":"L7MPRFEFQNHQ","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_16","alias_value":"L7MPRFEFQNHQLKPU","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_8","alias_value":"L7MPRFEF","created_at":"2026-05-18T12:32:33.847187+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/L7MPRFEFQNHQLKPUK2JCJEDZWP","json":"https://pith.science/pith/L7MPRFEFQNHQLKPUK2JCJEDZWP.json","graph_json":"https://pith.science/api/pith-number/L7MPRFEFQNHQLKPUK2JCJEDZWP/graph.json","events_json":"https://pith.science/api/pith-number/L7MPRFEFQNHQLKPUK2JCJEDZWP/events.json","paper":"https://pith.science/paper/L7MPRFEF"},"agent_actions":{"view_html":"https://pith.science/pith/L7MPRFEFQNHQLKPUK2JCJEDZWP","download_json":"https://pith.science/pith/L7MPRFEFQNHQLKPUK2JCJEDZWP.json","view_paper":"https://pith.science/paper/L7MPRFEF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.08952&json=true","fetch_graph":"https://pith.science/api/pith-number/L7MPRFEFQNHQLKPUK2JCJEDZWP/graph.json","fetch_events":"https://pith.science/api/pith-number/L7MPRFEFQNHQLKPUK2JCJEDZWP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L7MPRFEFQNHQLKPUK2JCJEDZWP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L7MPRFEFQNHQLKPUK2JCJEDZWP/action/storage_attestation","attest_author":"https://pith.science/pith/L7MPRFEFQNHQLKPUK2JCJEDZWP/action/author_attestation","sign_citation":"https://pith.science/pith/L7MPRFEFQNHQLKPUK2JCJEDZWP/action/citation_signature","submit_replication":"https://pith.science/pith/L7MPRFEFQNHQLKPUK2JCJEDZWP/action/replication_record"}},"created_at":"2026-05-18T00:15:08.974223+00:00","updated_at":"2026-05-18T00:15:08.974223+00:00"}