{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:6DQCCXWBYEIKAHGY6TSQYQWMWS","short_pith_number":"pith:6DQCCXWB","schema_version":"1.0","canonical_sha256":"f0e0215ec1c110a01cd8f4e50c42ccb4a3023fefc23fc096eb82a7ad60539094","source":{"kind":"arxiv","id":"1604.05978","version":2},"attestation_state":"computed","paper":{"title":"A topological insight into restricted Boltzmann machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SI"],"primary_cat":"cs.NE","authors_text":"Antonio Liotta, Decebal Constantin Mocanu, Elena Mocanu, Madeleine Gibescu, Phuong H. Nguyen","submitted_at":"2016-04-20T14:35:12Z","abstract_excerpt":"Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic features extraction, unsupervised weights initialization, but also as density estimators. Thus, their generative and discriminative capabilities, but also their computational time are instrumental to a wide range of applications. Our main contribution is to look at RBMs from a topological perspective, bringing insights from network science. Firstly, here we show that RBMs and Gaussian RBMs (GRBMs) are bipartite graphs which natu"},"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":"1604.05978","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-04-20T14:35:12Z","cross_cats_sorted":["cs.AI","cs.SI"],"title_canon_sha256":"ce4e2fc69eedcdef993295618dcad39b88712592d10e26ed09474f0d9df23c63","abstract_canon_sha256":"df4babef0bc6cebd14967a52d868d1eb8e9b9a94cf5134cb1a8040b0923bf184"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:10:55.381455Z","signature_b64":"VH3pVsmEK8TiuQx61y6WAT1ffdH4RWEyeC3WBuiGLKoclxjZE9AJ1hwuueudlgPCHT99wqlv8piS5UYoVq72Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0e0215ec1c110a01cd8f4e50c42ccb4a3023fefc23fc096eb82a7ad60539094","last_reissued_at":"2026-05-18T01:10:55.380844Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:10:55.380844Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A topological insight into restricted Boltzmann machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SI"],"primary_cat":"cs.NE","authors_text":"Antonio Liotta, Decebal Constantin Mocanu, Elena Mocanu, Madeleine Gibescu, Phuong H. Nguyen","submitted_at":"2016-04-20T14:35:12Z","abstract_excerpt":"Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic features extraction, unsupervised weights initialization, but also as density estimators. Thus, their generative and discriminative capabilities, but also their computational time are instrumental to a wide range of applications. Our main contribution is to look at RBMs from a topological perspective, bringing insights from network science. Firstly, here we show that RBMs and Gaussian RBMs (GRBMs) are bipartite graphs which natu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.05978","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":"1604.05978","created_at":"2026-05-18T01:10:55.380950+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.05978v2","created_at":"2026-05-18T01:10:55.380950+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.05978","created_at":"2026-05-18T01:10:55.380950+00:00"},{"alias_kind":"pith_short_12","alias_value":"6DQCCXWBYEIK","created_at":"2026-05-18T12:30:01.593930+00:00"},{"alias_kind":"pith_short_16","alias_value":"6DQCCXWBYEIKAHGY","created_at":"2026-05-18T12:30:01.593930+00:00"},{"alias_kind":"pith_short_8","alias_value":"6DQCCXWB","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/6DQCCXWBYEIKAHGY6TSQYQWMWS","json":"https://pith.science/pith/6DQCCXWBYEIKAHGY6TSQYQWMWS.json","graph_json":"https://pith.science/api/pith-number/6DQCCXWBYEIKAHGY6TSQYQWMWS/graph.json","events_json":"https://pith.science/api/pith-number/6DQCCXWBYEIKAHGY6TSQYQWMWS/events.json","paper":"https://pith.science/paper/6DQCCXWB"},"agent_actions":{"view_html":"https://pith.science/pith/6DQCCXWBYEIKAHGY6TSQYQWMWS","download_json":"https://pith.science/pith/6DQCCXWBYEIKAHGY6TSQYQWMWS.json","view_paper":"https://pith.science/paper/6DQCCXWB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.05978&json=true","fetch_graph":"https://pith.science/api/pith-number/6DQCCXWBYEIKAHGY6TSQYQWMWS/graph.json","fetch_events":"https://pith.science/api/pith-number/6DQCCXWBYEIKAHGY6TSQYQWMWS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6DQCCXWBYEIKAHGY6TSQYQWMWS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6DQCCXWBYEIKAHGY6TSQYQWMWS/action/storage_attestation","attest_author":"https://pith.science/pith/6DQCCXWBYEIKAHGY6TSQYQWMWS/action/author_attestation","sign_citation":"https://pith.science/pith/6DQCCXWBYEIKAHGY6TSQYQWMWS/action/citation_signature","submit_replication":"https://pith.science/pith/6DQCCXWBYEIKAHGY6TSQYQWMWS/action/replication_record"}},"created_at":"2026-05-18T01:10:55.380950+00:00","updated_at":"2026-05-18T01:10:55.380950+00:00"}