{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:VPBA6WIYP7MYAYXS5JIBG2GTBJ","short_pith_number":"pith:VPBA6WIY","schema_version":"1.0","canonical_sha256":"abc20f59187fd98062f2ea501368d30a6a29a6850c410918e3cc315fa098d617","source":{"kind":"arxiv","id":"2006.11868","version":1},"attestation_state":"computed","paper":{"title":"Generative models for sampling and phase transition indication in spin systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn"],"primary_cat":"cond-mat.stat-mech","authors_text":"Japneet Singh, Mathias S. Scheurer, Vinay Gupta, Vipul Arora","submitted_at":"2020-06-21T18:31:48Z","abstract_excerpt":"Recently, generative machine-learning models have gained popularity in physics, driven by the goal of improving the efficiency of Markov chain Monte Carlo techniques and of exploring their potential in capturing experimental data distributions. Motivated by their ability to generate images that look realistic to the human eye, we here study generative adversarial networks (GANs) as tools to learn the distribution of spin configurations and to generate samples, conditioned on external tuning parameters, such as temperature. We propose ways to efficiently represent the physical states, e.g., by "},"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":"2006.11868","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2020-06-21T18:31:48Z","cross_cats_sorted":["cond-mat.dis-nn"],"title_canon_sha256":"6db987530b5667766f52c2967a460d64b412b92ddabaf7c2eed6c90ce9b9a913","abstract_canon_sha256":"c190fbc3b8494966cb1ce5d0b33d36c59549472960caf27f0966c8fa5dc88a55"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:10:49.825305Z","signature_b64":"9jl/49p8cflejEKBGYSwhIdMSxr206XtrFOiQj9qKz+gXtZqJtYnEo327d5E+jX9jiXl5ksVySteXttef1T4Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"abc20f59187fd98062f2ea501368d30a6a29a6850c410918e3cc315fa098d617","last_reissued_at":"2026-07-05T03:10:49.824895Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:10:49.824895Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generative models for sampling and phase transition indication in spin systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn"],"primary_cat":"cond-mat.stat-mech","authors_text":"Japneet Singh, Mathias S. Scheurer, Vinay Gupta, Vipul Arora","submitted_at":"2020-06-21T18:31:48Z","abstract_excerpt":"Recently, generative machine-learning models have gained popularity in physics, driven by the goal of improving the efficiency of Markov chain Monte Carlo techniques and of exploring their potential in capturing experimental data distributions. Motivated by their ability to generate images that look realistic to the human eye, we here study generative adversarial networks (GANs) as tools to learn the distribution of spin configurations and to generate samples, conditioned on external tuning parameters, such as temperature. We propose ways to efficiently represent the physical states, e.g., by "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.11868","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.11868/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2006.11868","created_at":"2026-07-05T03:10:49.824964+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.11868v1","created_at":"2026-07-05T03:10:49.824964+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.11868","created_at":"2026-07-05T03:10:49.824964+00:00"},{"alias_kind":"pith_short_12","alias_value":"VPBA6WIYP7MY","created_at":"2026-07-05T03:10:49.824964+00:00"},{"alias_kind":"pith_short_16","alias_value":"VPBA6WIYP7MYAYXS","created_at":"2026-07-05T03:10:49.824964+00:00"},{"alias_kind":"pith_short_8","alias_value":"VPBA6WIY","created_at":"2026-07-05T03:10:49.824964+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/VPBA6WIYP7MYAYXS5JIBG2GTBJ","json":"https://pith.science/pith/VPBA6WIYP7MYAYXS5JIBG2GTBJ.json","graph_json":"https://pith.science/api/pith-number/VPBA6WIYP7MYAYXS5JIBG2GTBJ/graph.json","events_json":"https://pith.science/api/pith-number/VPBA6WIYP7MYAYXS5JIBG2GTBJ/events.json","paper":"https://pith.science/paper/VPBA6WIY"},"agent_actions":{"view_html":"https://pith.science/pith/VPBA6WIYP7MYAYXS5JIBG2GTBJ","download_json":"https://pith.science/pith/VPBA6WIYP7MYAYXS5JIBG2GTBJ.json","view_paper":"https://pith.science/paper/VPBA6WIY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.11868&json=true","fetch_graph":"https://pith.science/api/pith-number/VPBA6WIYP7MYAYXS5JIBG2GTBJ/graph.json","fetch_events":"https://pith.science/api/pith-number/VPBA6WIYP7MYAYXS5JIBG2GTBJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VPBA6WIYP7MYAYXS5JIBG2GTBJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VPBA6WIYP7MYAYXS5JIBG2GTBJ/action/storage_attestation","attest_author":"https://pith.science/pith/VPBA6WIYP7MYAYXS5JIBG2GTBJ/action/author_attestation","sign_citation":"https://pith.science/pith/VPBA6WIYP7MYAYXS5JIBG2GTBJ/action/citation_signature","submit_replication":"https://pith.science/pith/VPBA6WIYP7MYAYXS5JIBG2GTBJ/action/replication_record"}},"created_at":"2026-07-05T03:10:49.824964+00:00","updated_at":"2026-07-05T03:10:49.824964+00:00"}