{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:LBU3KV5JQVETBPMEMVNOJEM2KE","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":"d354f7453c6db1bd4385ebff70ba89473d9dd4c449f18bee0ddce2e2611a14e3","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"stat.ML","submitted_at":"2019-08-03T03:11:32Z","title_canon_sha256":"fdbc0898dcda3077c9d347dcf67a340b06671c675a20ed0cb7c1e68a0a7887e1"},"schema_version":"1.0","source":{"id":"1908.01113","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.01113","created_at":"2026-07-05T00:17:54Z"},{"alias_kind":"arxiv_version","alias_value":"1908.01113v1","created_at":"2026-07-05T00:17:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.01113","created_at":"2026-07-05T00:17:54Z"},{"alias_kind":"pith_short_12","alias_value":"LBU3KV5JQVET","created_at":"2026-07-05T00:17:54Z"},{"alias_kind":"pith_short_16","alias_value":"LBU3KV5JQVETBPME","created_at":"2026-07-05T00:17:54Z"},{"alias_kind":"pith_short_8","alias_value":"LBU3KV5J","created_at":"2026-07-05T00:17:54Z"}],"graph_snapshots":[{"event_id":"sha256:b4d82219a267df55fd4764c2e00fbd418313db544f310747e3c1955d30297388","target":"graph","created_at":"2026-07-05T00:17:54Z","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/1908.01113/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this study, an efficient stochastic gradient-free method, the ensemble neural networks (ENN), is developed. In the ENN, the optimization process relies on covariance matrices rather than derivatives. The covariance matrices are calculated by the ensemble randomized maximum likelihood algorithm (EnRML), which is an inverse modeling method. The ENN is able to simultaneously provide estimations and perform uncertainty quantification since it is built under the Bayesian framework. The ENN is also robust to small training data size because the ensemble of stochastic realizations essentially enla","authors_text":"Dongxiao Zhang, Haibin Chang, Meng Jin, Yuntian Chen","cross_cats":["cs.LG","stat.ME"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"stat.ML","submitted_at":"2019-08-03T03:11:32Z","title":"Ensemble Neural Networks (ENN): A gradient-free stochastic method"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.01113","kind":"arxiv","version":1},"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:f46b3d1e6a909da70c0f9fb6ccc0fb099587fdb698207ded13c0e1bc2ba07335","target":"record","created_at":"2026-07-05T00:17:54Z","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":"d354f7453c6db1bd4385ebff70ba89473d9dd4c449f18bee0ddce2e2611a14e3","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"stat.ML","submitted_at":"2019-08-03T03:11:32Z","title_canon_sha256":"fdbc0898dcda3077c9d347dcf67a340b06671c675a20ed0cb7c1e68a0a7887e1"},"schema_version":"1.0","source":{"id":"1908.01113","kind":"arxiv","version":1}},"canonical_sha256":"5869b557a9854930bd84655ae4919a51265910b27e8c3198db59ae2d14a75b92","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5869b557a9854930bd84655ae4919a51265910b27e8c3198db59ae2d14a75b92","first_computed_at":"2026-07-05T00:17:54.389447Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:17:54.389447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kZRfECwMaqf4Nm6Rz6f3EvN94lmIe+mG+zAq2Hi1ccAd1K2UxtNmzQHPs5geYURHIcKjIFWVU+NUsnV59pQiBA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:17:54.389909Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.01113","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f46b3d1e6a909da70c0f9fb6ccc0fb099587fdb698207ded13c0e1bc2ba07335","sha256:b4d82219a267df55fd4764c2e00fbd418313db544f310747e3c1955d30297388"],"state_sha256":"c7877515d1fa05fb7504c9b541e43d04944ed6449fbef0ae629b659050db1b3c"}