{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:D4WLCEZRW7EXBZBCI2TFQ6BYHR","short_pith_number":"pith:D4WLCEZR","schema_version":"1.0","canonical_sha256":"1f2cb11331b7c970e42246a65878383c66433cebbfc85baf363507c4110bc24c","source":{"kind":"arxiv","id":"2408.03323","version":1},"attestation_state":"computed","paper":{"title":"ClassiFIM: An Unsupervised Method To Detect Phase Transitions","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Daniel Lidar, Evgeny Mozgunov, Itay Hen, Nicholas Ezzell, Utkarsh Mishra, Victor Kasatkin","submitted_at":"2024-08-06T17:58:29Z","abstract_excerpt":"Estimation of the Fisher Information Metric (FIM-estimation) is an important task that arises in unsupervised learning of phase transitions, a problem proposed by physicists. This work completes the definition of the task by defining rigorous evaluation metrics distMSE, distMSEPS, and distRE and introduces ClassiFIM, a novel machine learning method designed to solve the FIM-estimation task. Unlike existing methods for unsupervised learning of phase transitions, ClassiFIM directly estimates a well-defined quantity (the FIM), allowing it to be rigorously compared to any present and future other "},"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":"2408.03323","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-08-06T17:58:29Z","cross_cats_sorted":[],"title_canon_sha256":"8a143ee61281aecc8b378b325c9c78863f9195970467ec6b7ea4a3650701426d","abstract_canon_sha256":"54d0aff6e74586873021f008847cfe14f6ad29a6653e48127c79f4cf00543d34"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:52:48.957962Z","signature_b64":"E7Dl+DeVrYeqm02ymXVEe/NaPbdhZ/K6ORUhbrBZJqFZo2z49UAVWwFUQmHxhMORMH4dxG3c5/pvypBkQ7AuDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f2cb11331b7c970e42246a65878383c66433cebbfc85baf363507c4110bc24c","last_reissued_at":"2026-07-05T08:52:48.957573Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:52:48.957573Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ClassiFIM: An Unsupervised Method To Detect Phase Transitions","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Daniel Lidar, Evgeny Mozgunov, Itay Hen, Nicholas Ezzell, Utkarsh Mishra, Victor Kasatkin","submitted_at":"2024-08-06T17:58:29Z","abstract_excerpt":"Estimation of the Fisher Information Metric (FIM-estimation) is an important task that arises in unsupervised learning of phase transitions, a problem proposed by physicists. This work completes the definition of the task by defining rigorous evaluation metrics distMSE, distMSEPS, and distRE and introduces ClassiFIM, a novel machine learning method designed to solve the FIM-estimation task. Unlike existing methods for unsupervised learning of phase transitions, ClassiFIM directly estimates a well-defined quantity (the FIM), allowing it to be rigorously compared to any present and future other "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.03323","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/2408.03323/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":"2408.03323","created_at":"2026-07-05T08:52:48.957629+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.03323v1","created_at":"2026-07-05T08:52:48.957629+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.03323","created_at":"2026-07-05T08:52:48.957629+00:00"},{"alias_kind":"pith_short_12","alias_value":"D4WLCEZRW7EX","created_at":"2026-07-05T08:52:48.957629+00:00"},{"alias_kind":"pith_short_16","alias_value":"D4WLCEZRW7EXBZBC","created_at":"2026-07-05T08:52:48.957629+00:00"},{"alias_kind":"pith_short_8","alias_value":"D4WLCEZR","created_at":"2026-07-05T08:52:48.957629+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.06678","citing_title":"Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection","ref_index":33,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D4WLCEZRW7EXBZBCI2TFQ6BYHR","json":"https://pith.science/pith/D4WLCEZRW7EXBZBCI2TFQ6BYHR.json","graph_json":"https://pith.science/api/pith-number/D4WLCEZRW7EXBZBCI2TFQ6BYHR/graph.json","events_json":"https://pith.science/api/pith-number/D4WLCEZRW7EXBZBCI2TFQ6BYHR/events.json","paper":"https://pith.science/paper/D4WLCEZR"},"agent_actions":{"view_html":"https://pith.science/pith/D4WLCEZRW7EXBZBCI2TFQ6BYHR","download_json":"https://pith.science/pith/D4WLCEZRW7EXBZBCI2TFQ6BYHR.json","view_paper":"https://pith.science/paper/D4WLCEZR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.03323&json=true","fetch_graph":"https://pith.science/api/pith-number/D4WLCEZRW7EXBZBCI2TFQ6BYHR/graph.json","fetch_events":"https://pith.science/api/pith-number/D4WLCEZRW7EXBZBCI2TFQ6BYHR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D4WLCEZRW7EXBZBCI2TFQ6BYHR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D4WLCEZRW7EXBZBCI2TFQ6BYHR/action/storage_attestation","attest_author":"https://pith.science/pith/D4WLCEZRW7EXBZBCI2TFQ6BYHR/action/author_attestation","sign_citation":"https://pith.science/pith/D4WLCEZRW7EXBZBCI2TFQ6BYHR/action/citation_signature","submit_replication":"https://pith.science/pith/D4WLCEZRW7EXBZBCI2TFQ6BYHR/action/replication_record"}},"created_at":"2026-07-05T08:52:48.957629+00:00","updated_at":"2026-07-05T08:52:48.957629+00:00"}