{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:EBCDQKJ3NT42PRLKTTZH7HFLMU","short_pith_number":"pith:EBCDQKJ3","canonical_record":{"source":{"id":"1902.03932","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T15:03:30Z","cross_cats_sorted":["cs.AI","cs.CV","stat.ME","stat.ML"],"title_canon_sha256":"1786331b988e200c0da1185e88c2970c52aeab038f3032ee4096f7fca9d488a2","abstract_canon_sha256":"cd8f0b19b41da0e9d699431aea6ade84be5b4dfbb4a457b5a13f236d6ad5c87d"},"schema_version":"1.0"},"canonical_sha256":"204438293b6cf9a7c56a9cf27f9cab651af5579438e78a5b855ec0e814bb0c7c","source":{"kind":"arxiv","id":"1902.03932","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.03932","created_at":"2026-07-05T01:02:00Z"},{"alias_kind":"arxiv_version","alias_value":"1902.03932v2","created_at":"2026-07-05T01:02:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.03932","created_at":"2026-07-05T01:02:00Z"},{"alias_kind":"pith_short_12","alias_value":"EBCDQKJ3NT42","created_at":"2026-07-05T01:02:00Z"},{"alias_kind":"pith_short_16","alias_value":"EBCDQKJ3NT42PRLK","created_at":"2026-07-05T01:02:00Z"},{"alias_kind":"pith_short_8","alias_value":"EBCDQKJ3","created_at":"2026-07-05T01:02:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:EBCDQKJ3NT42PRLKTTZH7HFLMU","target":"record","payload":{"canonical_record":{"source":{"id":"1902.03932","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T15:03:30Z","cross_cats_sorted":["cs.AI","cs.CV","stat.ME","stat.ML"],"title_canon_sha256":"1786331b988e200c0da1185e88c2970c52aeab038f3032ee4096f7fca9d488a2","abstract_canon_sha256":"cd8f0b19b41da0e9d699431aea6ade84be5b4dfbb4a457b5a13f236d6ad5c87d"},"schema_version":"1.0"},"canonical_sha256":"204438293b6cf9a7c56a9cf27f9cab651af5579438e78a5b855ec0e814bb0c7c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:02:00.852684Z","signature_b64":"H8l/Xxw4bjo3ZrEh3bubzkrMvumaDOZEIAS1g4eopnPfNFJLdiTy7ESc3LOZYIykllKf93sjmEIXSkovLeUFAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"204438293b6cf9a7c56a9cf27f9cab651af5579438e78a5b855ec0e814bb0c7c","last_reissued_at":"2026-07-05T01:02:00.852271Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:02:00.852271Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.03932","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:02:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PYaKsA6B8iV9nE++Q1DyBS2C9oIAcYNw/SZk3AeSqpMYJCJfMq3vapwDEpML8hZ+waDgcKmyqeNfrh45FzW6Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:45:58.759179Z"},"content_sha256":"dd3ba6df545e04c6c98cfcbf47090326080f4f8b6982e3de2a194b0cfd9f134d","schema_version":"1.0","event_id":"sha256:dd3ba6df545e04c6c98cfcbf47090326080f4f8b6982e3de2a194b0cfd9f134d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:EBCDQKJ3NT42PRLKTTZH7HFLMU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrew Gordon Wilson, Changyou Chen, Chunyuan Li, Jianyi Zhang, Ruqi Zhang","submitted_at":"2019-02-11T15:03:30Z","abstract_excerpt":"The posteriors over neural network weights are high dimensional and multimodal. Each mode typically characterizes a meaningfully different representation of the data. We develop Cyclical Stochastic Gradient MCMC (SG-MCMC) to automatically explore such distributions. In particular, we propose a cyclical stepsize schedule, where larger steps discover new modes, and smaller steps characterize each mode. We also prove non-asymptotic convergence of our proposed algorithm. Moreover, we provide extensive experimental results, including ImageNet, to demonstrate the scalability and effectiveness of cyc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.03932","kind":"arxiv","version":2},"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/1902.03932/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:02:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0rXwqVQ9tzw2eIkswkR2O1YpyJSrC6e6Zv6beKvatOZ3NjmzBaLnEciTwfV/RRE0SlEZNLaYtAXRt/a9IycqAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:45:58.759732Z"},"content_sha256":"1cc1e2690527b41212f471f7bfe61ac0d6a699785b7ed7ec1edbdc3afcc60ad1","schema_version":"1.0","event_id":"sha256:1cc1e2690527b41212f471f7bfe61ac0d6a699785b7ed7ec1edbdc3afcc60ad1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EBCDQKJ3NT42PRLKTTZH7HFLMU/bundle.json","state_url":"https://pith.science/pith/EBCDQKJ3NT42PRLKTTZH7HFLMU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EBCDQKJ3NT42PRLKTTZH7HFLMU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-03T19:45:58Z","links":{"resolver":"https://pith.science/pith/EBCDQKJ3NT42PRLKTTZH7HFLMU","bundle":"https://pith.science/pith/EBCDQKJ3NT42PRLKTTZH7HFLMU/bundle.json","state":"https://pith.science/pith/EBCDQKJ3NT42PRLKTTZH7HFLMU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EBCDQKJ3NT42PRLKTTZH7HFLMU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:EBCDQKJ3NT42PRLKTTZH7HFLMU","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":"cd8f0b19b41da0e9d699431aea6ade84be5b4dfbb4a457b5a13f236d6ad5c87d","cross_cats_sorted":["cs.AI","cs.CV","stat.ME","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T15:03:30Z","title_canon_sha256":"1786331b988e200c0da1185e88c2970c52aeab038f3032ee4096f7fca9d488a2"},"schema_version":"1.0","source":{"id":"1902.03932","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.03932","created_at":"2026-07-05T01:02:00Z"},{"alias_kind":"arxiv_version","alias_value":"1902.03932v2","created_at":"2026-07-05T01:02:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.03932","created_at":"2026-07-05T01:02:00Z"},{"alias_kind":"pith_short_12","alias_value":"EBCDQKJ3NT42","created_at":"2026-07-05T01:02:00Z"},{"alias_kind":"pith_short_16","alias_value":"EBCDQKJ3NT42PRLK","created_at":"2026-07-05T01:02:00Z"},{"alias_kind":"pith_short_8","alias_value":"EBCDQKJ3","created_at":"2026-07-05T01:02:00Z"}],"graph_snapshots":[{"event_id":"sha256:1cc1e2690527b41212f471f7bfe61ac0d6a699785b7ed7ec1edbdc3afcc60ad1","target":"graph","created_at":"2026-07-05T01:02:00Z","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/1902.03932/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The posteriors over neural network weights are high dimensional and multimodal. Each mode typically characterizes a meaningfully different representation of the data. We develop Cyclical Stochastic Gradient MCMC (SG-MCMC) to automatically explore such distributions. In particular, we propose a cyclical stepsize schedule, where larger steps discover new modes, and smaller steps characterize each mode. We also prove non-asymptotic convergence of our proposed algorithm. Moreover, we provide extensive experimental results, including ImageNet, to demonstrate the scalability and effectiveness of cyc","authors_text":"Andrew Gordon Wilson, Changyou Chen, Chunyuan Li, Jianyi Zhang, Ruqi Zhang","cross_cats":["cs.AI","cs.CV","stat.ME","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T15:03:30Z","title":"Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.03932","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:dd3ba6df545e04c6c98cfcbf47090326080f4f8b6982e3de2a194b0cfd9f134d","target":"record","created_at":"2026-07-05T01:02:00Z","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":"cd8f0b19b41da0e9d699431aea6ade84be5b4dfbb4a457b5a13f236d6ad5c87d","cross_cats_sorted":["cs.AI","cs.CV","stat.ME","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T15:03:30Z","title_canon_sha256":"1786331b988e200c0da1185e88c2970c52aeab038f3032ee4096f7fca9d488a2"},"schema_version":"1.0","source":{"id":"1902.03932","kind":"arxiv","version":2}},"canonical_sha256":"204438293b6cf9a7c56a9cf27f9cab651af5579438e78a5b855ec0e814bb0c7c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"204438293b6cf9a7c56a9cf27f9cab651af5579438e78a5b855ec0e814bb0c7c","first_computed_at":"2026-07-05T01:02:00.852271Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:02:00.852271Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"H8l/Xxw4bjo3ZrEh3bubzkrMvumaDOZEIAS1g4eopnPfNFJLdiTy7ESc3LOZYIykllKf93sjmEIXSkovLeUFAA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:02:00.852684Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.03932","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dd3ba6df545e04c6c98cfcbf47090326080f4f8b6982e3de2a194b0cfd9f134d","sha256:1cc1e2690527b41212f471f7bfe61ac0d6a699785b7ed7ec1edbdc3afcc60ad1"],"state_sha256":"4e10fdf3f4a237b01fe00aaa8a1e801d7a00425a6215cff8bb495f7d3a47858b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2cToiV7EVrNIGSYgpM4hcXG4dnpCluirtuj9ASpiBb/ZuGBJ6fV4iX5pXgkD+GIC6+hsqlODKOrdns1jffKFBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T19:45:58.763458Z","bundle_sha256":"58885154bc94bb3806b27dfd02a6621972603ff04260e919e5de0c36bd53274d"}}