{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:7IPP4OGQXXTOZKKX5QEJ3EZF47","short_pith_number":"pith:7IPP4OGQ","schema_version":"1.0","canonical_sha256":"fa1efe38d0bde6eca957ec089d9325e7fa16dadb7e3b73d5570fa7ba66dba8db","source":{"kind":"arxiv","id":"1710.04874","version":1},"attestation_state":"computed","paper":{"title":"A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.NE","authors_text":"Grzegorz Dudek","submitted_at":"2017-10-13T11:23:18Z","abstract_excerpt":"Neural networks with random hidden nodes have gained increasing interest from researchers and practical applications. This is due to their unique features such as very fast training and universal approximation property. In these networks the weights and biases of hidden nodes determining the nonlinear feature mapping are set randomly and are not learned. Appropriate selection of the intervals from which weights and biases are selected is extremely important. This topic has not yet been sufficiently explored in the literature. In this work a method of generating random weights and biases is pro"},"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":"1710.04874","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-10-13T11:23:18Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"66b8a42c242c69b1bd79833c5933be0690cbe6e410076e5ad458406f7ce2e8be","abstract_canon_sha256":"99cb59015169abaec8a95b405b6146b460dc0730e8c4d7d8f59ed72bbc683ab6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:56.454818Z","signature_b64":"k0f+nvdD5vAPtVGbNWiKQ3Ityh2/jsVE3E4qvnVyc+aXY9fxxCinu8IqVpRGsplD8082Y5VK13Y2b1Luc359BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fa1efe38d0bde6eca957ec089d9325e7fa16dadb7e3b73d5570fa7ba66dba8db","last_reissued_at":"2026-05-18T00:32:56.454232Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:56.454232Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.NE","authors_text":"Grzegorz Dudek","submitted_at":"2017-10-13T11:23:18Z","abstract_excerpt":"Neural networks with random hidden nodes have gained increasing interest from researchers and practical applications. This is due to their unique features such as very fast training and universal approximation property. In these networks the weights and biases of hidden nodes determining the nonlinear feature mapping are set randomly and are not learned. Appropriate selection of the intervals from which weights and biases are selected is extremely important. This topic has not yet been sufficiently explored in the literature. In this work a method of generating random weights and biases is pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04874","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":"1710.04874","created_at":"2026-05-18T00:32:56.454331+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.04874v1","created_at":"2026-05-18T00:32:56.454331+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04874","created_at":"2026-05-18T00:32:56.454331+00:00"},{"alias_kind":"pith_short_12","alias_value":"7IPP4OGQXXTO","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"7IPP4OGQXXTOZKKX","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"7IPP4OGQ","created_at":"2026-05-18T12:31:05.417338+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/7IPP4OGQXXTOZKKX5QEJ3EZF47","json":"https://pith.science/pith/7IPP4OGQXXTOZKKX5QEJ3EZF47.json","graph_json":"https://pith.science/api/pith-number/7IPP4OGQXXTOZKKX5QEJ3EZF47/graph.json","events_json":"https://pith.science/api/pith-number/7IPP4OGQXXTOZKKX5QEJ3EZF47/events.json","paper":"https://pith.science/paper/7IPP4OGQ"},"agent_actions":{"view_html":"https://pith.science/pith/7IPP4OGQXXTOZKKX5QEJ3EZF47","download_json":"https://pith.science/pith/7IPP4OGQXXTOZKKX5QEJ3EZF47.json","view_paper":"https://pith.science/paper/7IPP4OGQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.04874&json=true","fetch_graph":"https://pith.science/api/pith-number/7IPP4OGQXXTOZKKX5QEJ3EZF47/graph.json","fetch_events":"https://pith.science/api/pith-number/7IPP4OGQXXTOZKKX5QEJ3EZF47/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7IPP4OGQXXTOZKKX5QEJ3EZF47/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7IPP4OGQXXTOZKKX5QEJ3EZF47/action/storage_attestation","attest_author":"https://pith.science/pith/7IPP4OGQXXTOZKKX5QEJ3EZF47/action/author_attestation","sign_citation":"https://pith.science/pith/7IPP4OGQXXTOZKKX5QEJ3EZF47/action/citation_signature","submit_replication":"https://pith.science/pith/7IPP4OGQXXTOZKKX5QEJ3EZF47/action/replication_record"}},"created_at":"2026-05-18T00:32:56.454331+00:00","updated_at":"2026-05-18T00:32:56.454331+00:00"}