{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:R3C6GEGVB4QN5ICKZP5A5GPVM4","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":"5578ce7330a6a1610bf87a8b634adae8fe8fb47643d501fe3aca003211b42476","cross_cats_sorted":["cond-mat.stat-mech","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-16T01:01:55Z","title_canon_sha256":"b4db11fd906babe6875cd141d82f2d55bb89b21454e9607da39f98a2e60937e3"},"schema_version":"1.0","source":{"id":"2205.07408","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.07408","created_at":"2026-07-05T04:47:28Z"},{"alias_kind":"arxiv_version","alias_value":"2205.07408v2","created_at":"2026-07-05T04:47:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.07408","created_at":"2026-07-05T04:47:28Z"},{"alias_kind":"pith_short_12","alias_value":"R3C6GEGVB4QN","created_at":"2026-07-05T04:47:28Z"},{"alias_kind":"pith_short_16","alias_value":"R3C6GEGVB4QN5ICK","created_at":"2026-07-05T04:47:28Z"},{"alias_kind":"pith_short_8","alias_value":"R3C6GEGV","created_at":"2026-07-05T04:47:28Z"}],"graph_snapshots":[{"event_id":"sha256:c24f629df2c4d8297f33f27661c58012e1374969a81ec44988559e5da0047f56","target":"graph","created_at":"2026-07-05T04:47:28Z","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/2205.07408/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We examine the zero-temperature Metropolis Monte Carlo algorithm as a tool for training a neural network by minimizing a loss function. We find that, as expected on theoretical grounds and shown empirically by other authors, Metropolis Monte Carlo can train a neural net with an accuracy comparable to that of gradient descent, if not necessarily as quickly. The Metropolis algorithm does not fail automatically when the number of parameters of a neural network is large. It can fail when a neural network's structure or neuron activations are strongly heterogenous, and we introduce an adaptive Mont","authors_text":"Corneel Casert, Ian Benlolo, Isaac Tamblyn, Stephen Whitelam, Viktor Selin","cross_cats":["cond-mat.stat-mech","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-16T01:01:55Z","title":"Training neural networks using Metropolis Monte Carlo and an adaptive variant"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.07408","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":"sha256:553da319fadb5025f59610bab694a55fefe3bf42de90cb0f3baa4247450a67a4","target":"record","created_at":"2026-07-05T04:47:28Z","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":"5578ce7330a6a1610bf87a8b634adae8fe8fb47643d501fe3aca003211b42476","cross_cats_sorted":["cond-mat.stat-mech","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-16T01:01:55Z","title_canon_sha256":"b4db11fd906babe6875cd141d82f2d55bb89b21454e9607da39f98a2e60937e3"},"schema_version":"1.0","source":{"id":"2205.07408","kind":"arxiv","version":2}},"canonical_sha256":"8ec5e310d50f20dea04acbfa0e99f5671bf95aa5e2ddb0d36592b64cc717a92a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8ec5e310d50f20dea04acbfa0e99f5671bf95aa5e2ddb0d36592b64cc717a92a","first_computed_at":"2026-07-05T04:47:28.431870Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:47:28.431870Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dlN2atvyXKqihNv743nRVbfLhnoyaCmOaOscGPTMGbn/UPc8ictG/OTH83vGsXgp1YQ99lYSMnWEJcCcTMPfBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:47:28.432297Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.07408","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:553da319fadb5025f59610bab694a55fefe3bf42de90cb0f3baa4247450a67a4","sha256:c24f629df2c4d8297f33f27661c58012e1374969a81ec44988559e5da0047f56"],"state_sha256":"35cfcba5384ec6bfee334a8f8bfb235982a4f266e982a48a8220b24e239e708e"}