{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:6D372FK7C5HLFZH34ASLF6OX4O","short_pith_number":"pith:6D372FK7","schema_version":"1.0","canonical_sha256":"f0f7fd155f174eb2e4fbe024b2f9d7e3916b529403d07f4dbfbac9bce6d86582","source":{"kind":"arxiv","id":"1609.04846","version":1},"attestation_state":"computed","paper":{"title":"A Tutorial about Random Neural Networks in Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Gerardo Rubino, Sebasti\\'an Basterrech","submitted_at":"2016-09-15T20:21:30Z","abstract_excerpt":"Random Neural Networks (RNNs) are a class of Neural Networks (NNs) that can also be seen as a specific type of queuing network. They have been successfully used in several domains during the last 25 years, as queuing networks to analyze the performance of resource sharing in many engineering areas, as learning tools and in combinatorial optimization, where they are seen as neural systems, and also as models of neurological aspects of living beings. In this article we focus on their learning capabilities, and more specifically, we present a practical guide for using the RNN to solve supervised "},"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":"1609.04846","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-09-15T20:21:30Z","cross_cats_sorted":[],"title_canon_sha256":"8d588b0e588393b1328ba48e732f90b336c47768eec5dc98f8bf9cfbffc0f8fd","abstract_canon_sha256":"ddf89cb68139d3996c832e672c7939986bfdbfc30539e88bbdd64f5b43baef06"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:04:32.298409Z","signature_b64":"1aODEfANfkJMosjfNDVMN4xkU9X7N286Ch23KbPJUFUiS1lCcjaewDclypg1vUpN7HXWDnuAQInAe0CjwK/VDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0f7fd155f174eb2e4fbe024b2f9d7e3916b529403d07f4dbfbac9bce6d86582","last_reissued_at":"2026-05-18T01:04:32.297617Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:04:32.297617Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Tutorial about Random Neural Networks in Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Gerardo Rubino, Sebasti\\'an Basterrech","submitted_at":"2016-09-15T20:21:30Z","abstract_excerpt":"Random Neural Networks (RNNs) are a class of Neural Networks (NNs) that can also be seen as a specific type of queuing network. They have been successfully used in several domains during the last 25 years, as queuing networks to analyze the performance of resource sharing in many engineering areas, as learning tools and in combinatorial optimization, where they are seen as neural systems, and also as models of neurological aspects of living beings. In this article we focus on their learning capabilities, and more specifically, we present a practical guide for using the RNN to solve supervised "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.04846","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":"1609.04846","created_at":"2026-05-18T01:04:32.297755+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.04846v1","created_at":"2026-05-18T01:04:32.297755+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.04846","created_at":"2026-05-18T01:04:32.297755+00:00"},{"alias_kind":"pith_short_12","alias_value":"6D372FK7C5HL","created_at":"2026-05-18T12:30:01.593930+00:00"},{"alias_kind":"pith_short_16","alias_value":"6D372FK7C5HLFZH3","created_at":"2026-05-18T12:30:01.593930+00:00"},{"alias_kind":"pith_short_8","alias_value":"6D372FK7","created_at":"2026-05-18T12:30:01.593930+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/6D372FK7C5HLFZH34ASLF6OX4O","json":"https://pith.science/pith/6D372FK7C5HLFZH34ASLF6OX4O.json","graph_json":"https://pith.science/api/pith-number/6D372FK7C5HLFZH34ASLF6OX4O/graph.json","events_json":"https://pith.science/api/pith-number/6D372FK7C5HLFZH34ASLF6OX4O/events.json","paper":"https://pith.science/paper/6D372FK7"},"agent_actions":{"view_html":"https://pith.science/pith/6D372FK7C5HLFZH34ASLF6OX4O","download_json":"https://pith.science/pith/6D372FK7C5HLFZH34ASLF6OX4O.json","view_paper":"https://pith.science/paper/6D372FK7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.04846&json=true","fetch_graph":"https://pith.science/api/pith-number/6D372FK7C5HLFZH34ASLF6OX4O/graph.json","fetch_events":"https://pith.science/api/pith-number/6D372FK7C5HLFZH34ASLF6OX4O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6D372FK7C5HLFZH34ASLF6OX4O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6D372FK7C5HLFZH34ASLF6OX4O/action/storage_attestation","attest_author":"https://pith.science/pith/6D372FK7C5HLFZH34ASLF6OX4O/action/author_attestation","sign_citation":"https://pith.science/pith/6D372FK7C5HLFZH34ASLF6OX4O/action/citation_signature","submit_replication":"https://pith.science/pith/6D372FK7C5HLFZH34ASLF6OX4O/action/replication_record"}},"created_at":"2026-05-18T01:04:32.297755+00:00","updated_at":"2026-05-18T01:04:32.297755+00:00"}