{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:6U3CJNTA6RSFDJF4DF2VI3VIBN","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"632f4a06abc3484df0e900f17a0437326740c472371f3f392f0b01efbf674a3b","cross_cats_sorted":["cond-mat.stat-mech","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2020-01-15T15:04:21Z","title_canon_sha256":"26c052604705537caa6675ab75976253647e6dda8a233f8f056db2604158e647"},"schema_version":"1.0","source":{"id":"2001.05361","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.05361","created_at":"2026-07-05T01:08:26Z"},{"alias_kind":"arxiv_version","alias_value":"2001.05361v2","created_at":"2026-07-05T01:08:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.05361","created_at":"2026-07-05T01:08:26Z"},{"alias_kind":"pith_short_12","alias_value":"6U3CJNTA6RSF","created_at":"2026-07-05T01:08:26Z"},{"alias_kind":"pith_short_16","alias_value":"6U3CJNTA6RSFDJF4","created_at":"2026-07-05T01:08:26Z"},{"alias_kind":"pith_short_8","alias_value":"6U3CJNTA","created_at":"2026-07-05T01:08:26Z"}],"graph_snapshots":[{"event_id":"sha256:8a3d437d14d61f6a22f0583c950c649b7bfda82d2a13a7d91a448f8de1c3f5b7","target":"graph","created_at":"2026-07-05T01:08:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2001.05361/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in deep learning and neural networks have led to an increased interest in the application of generative models in statistical and condensed matter physics. In particular, restricted Boltzmann machines (RBMs) and variational autoencoders (VAEs) as specific classes of neural networks have been successfully applied in the context of physical feature extraction and representation learning. Despite these successes, however, there is only limited understanding of their representational properties and limitations. To better understand the representational characteristics of RBMs and V","authors_text":"Francesco D'Angelo, Lucas B\\\"ottcher","cross_cats":["cond-mat.stat-mech","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2020-01-15T15:04:21Z","title":"Learning the Ising Model with Generative Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.05361","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f34b9846e772b4661667a0449daf566a61238ec815c1dd4c08dc41deec6c34f3","target":"record","created_at":"2026-07-05T01:08:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"632f4a06abc3484df0e900f17a0437326740c472371f3f392f0b01efbf674a3b","cross_cats_sorted":["cond-mat.stat-mech","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2020-01-15T15:04:21Z","title_canon_sha256":"26c052604705537caa6675ab75976253647e6dda8a233f8f056db2604158e647"},"schema_version":"1.0","source":{"id":"2001.05361","kind":"arxiv","version":2}},"canonical_sha256":"f53624b660f46451a4bc1975546ea80b6043322f2f52442b39c23a17cfef6320","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f53624b660f46451a4bc1975546ea80b6043322f2f52442b39c23a17cfef6320","first_computed_at":"2026-07-05T01:08:26.445088Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:08:26.445088Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YZPSSnAwJq5d/s13ViH5ZxNtWrpuA4fO0LT/i7NY9QFvi52PeImxMBY3fnp6PfA50lZq0BgDIRLmz+okuslgDg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:08:26.445455Z","signed_message":"canonical_sha256_bytes"},"source_id":"2001.05361","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f34b9846e772b4661667a0449daf566a61238ec815c1dd4c08dc41deec6c34f3","sha256:8a3d437d14d61f6a22f0583c950c649b7bfda82d2a13a7d91a448f8de1c3f5b7"],"state_sha256":"9c374f83985b6e58b6dc774f30a69e5cccee17b14bade313042ce9456fe49114"}