{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:KLU7SZBEGCCYM5FMET3DVH5Y4Q","short_pith_number":"pith:KLU7SZBE","schema_version":"1.0","canonical_sha256":"52e9f9642430858674ac24f63a9fb8e40caab0cdb13e37b8cf606041583bbb0f","source":{"kind":"arxiv","id":"1210.5474","version":1},"attestation_state":"computed","paper":{"title":"Disentangling Factors of Variation via Generative Entangling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Aaron Courville, Guillaume Desjardins, Yoshua Bengio","submitted_at":"2012-10-19T17:16:48Z","abstract_excerpt":"Here we propose a novel model family with the objective of learning to disentangle the factors of variation in data. Our approach is based on the spike-and-slab restricted Boltzmann machine which we generalize to include higher-order interactions among multiple latent variables. Seen from a generative perspective, the multiplicative interactions emulates the entangling of factors of variation. Inference in the model can be seen as disentangling these generative factors. Unlike previous attempts at disentangling latent factors, the proposed model is trained using no supervised information regar"},"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":"1210.5474","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-10-19T17:16:48Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"6a1fea57999447343acedec4f87700f7c3f07acd21be629d94839ded7eb647f2","abstract_canon_sha256":"a5ffc0ca740182eae16600cc9fe69617d863b808c550a463f0ededf978d0a10d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:42:45.219785Z","signature_b64":"38u1NpQ+1ilQEWovBVm2RR0uM/Rmg6WNaB7tg9sddY8poU2qPYTL+e9mAHtnRj1+cNxavXSVPUEA688XbNRDCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"52e9f9642430858674ac24f63a9fb8e40caab0cdb13e37b8cf606041583bbb0f","last_reissued_at":"2026-05-18T03:42:45.219210Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:42:45.219210Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Disentangling Factors of Variation via Generative Entangling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Aaron Courville, Guillaume Desjardins, Yoshua Bengio","submitted_at":"2012-10-19T17:16:48Z","abstract_excerpt":"Here we propose a novel model family with the objective of learning to disentangle the factors of variation in data. Our approach is based on the spike-and-slab restricted Boltzmann machine which we generalize to include higher-order interactions among multiple latent variables. Seen from a generative perspective, the multiplicative interactions emulates the entangling of factors of variation. Inference in the model can be seen as disentangling these generative factors. Unlike previous attempts at disentangling latent factors, the proposed model is trained using no supervised information regar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1210.5474","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":"1210.5474","created_at":"2026-05-18T03:42:45.219305+00:00"},{"alias_kind":"arxiv_version","alias_value":"1210.5474v1","created_at":"2026-05-18T03:42:45.219305+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1210.5474","created_at":"2026-05-18T03:42:45.219305+00:00"},{"alias_kind":"pith_short_12","alias_value":"KLU7SZBEGCCY","created_at":"2026-05-18T12:27:11.947152+00:00"},{"alias_kind":"pith_short_16","alias_value":"KLU7SZBEGCCYM5FM","created_at":"2026-05-18T12:27:11.947152+00:00"},{"alias_kind":"pith_short_8","alias_value":"KLU7SZBE","created_at":"2026-05-18T12:27:11.947152+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2403.19647","citing_title":"Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KLU7SZBEGCCYM5FMET3DVH5Y4Q","json":"https://pith.science/pith/KLU7SZBEGCCYM5FMET3DVH5Y4Q.json","graph_json":"https://pith.science/api/pith-number/KLU7SZBEGCCYM5FMET3DVH5Y4Q/graph.json","events_json":"https://pith.science/api/pith-number/KLU7SZBEGCCYM5FMET3DVH5Y4Q/events.json","paper":"https://pith.science/paper/KLU7SZBE"},"agent_actions":{"view_html":"https://pith.science/pith/KLU7SZBEGCCYM5FMET3DVH5Y4Q","download_json":"https://pith.science/pith/KLU7SZBEGCCYM5FMET3DVH5Y4Q.json","view_paper":"https://pith.science/paper/KLU7SZBE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1210.5474&json=true","fetch_graph":"https://pith.science/api/pith-number/KLU7SZBEGCCYM5FMET3DVH5Y4Q/graph.json","fetch_events":"https://pith.science/api/pith-number/KLU7SZBEGCCYM5FMET3DVH5Y4Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KLU7SZBEGCCYM5FMET3DVH5Y4Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KLU7SZBEGCCYM5FMET3DVH5Y4Q/action/storage_attestation","attest_author":"https://pith.science/pith/KLU7SZBEGCCYM5FMET3DVH5Y4Q/action/author_attestation","sign_citation":"https://pith.science/pith/KLU7SZBEGCCYM5FMET3DVH5Y4Q/action/citation_signature","submit_replication":"https://pith.science/pith/KLU7SZBEGCCYM5FMET3DVH5Y4Q/action/replication_record"}},"created_at":"2026-05-18T03:42:45.219305+00:00","updated_at":"2026-05-18T03:42:45.219305+00:00"}