{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:MVPMJFKNRAWMYPILCKF4O4SUFD","short_pith_number":"pith:MVPMJFKN","schema_version":"1.0","canonical_sha256":"655ec4954d882ccc3d0b128bc7725428f8d958e2e54bf4f6a874f0f887265f3b","source":{"kind":"arxiv","id":"2004.10710","version":3},"attestation_state":"computed","paper":{"title":"Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph","stat.ML"],"primary_cat":"cs.LG","authors_text":"Brian Nord, Jo\\~ao Caldeira","submitted_at":"2020-04-22T17:13:15Z","abstract_excerpt":"We present a comparison of methods for uncertainty quantification (UQ) in deep learning algorithms in the context of a simple physical system. Three of the most common uncertainty quantification methods - Bayesian Neural Networks (BNN), Concrete Dropout (CD), and Deep Ensembles (DE) - are compared to the standard analytic error propagation. We discuss this comparison in terms endemic to both machine learning (\"epistemic\" and \"aleatoric\") and the physical sciences (\"statistical\" and \"systematic\"). The comparisons are presented in terms of simulated experimental measurements of a single pendulum"},"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":"2004.10710","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-04-22T17:13:15Z","cross_cats_sorted":["physics.comp-ph","stat.ML"],"title_canon_sha256":"b447e36a71fcf8b33bcab0266cec425dac9a3f373b76e4903281168d8831c790","abstract_canon_sha256":"cfd58e368fbfed267df77bbe966c84be3730be92344868674f30dd9c38a71738"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:57:16.347341Z","signature_b64":"SgTpRa7NllIMHbWIIvGdmEf2XXm514eXCARLIUweZlpX3BnIUJOgGXVWW+Ba3KG49FrzbEAapkAtxdWjSPXIBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"655ec4954d882ccc3d0b128bc7725428f8d958e2e54bf4f6a874f0f887265f3b","last_reissued_at":"2026-07-05T01:57:16.346777Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:57:16.346777Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph","stat.ML"],"primary_cat":"cs.LG","authors_text":"Brian Nord, Jo\\~ao Caldeira","submitted_at":"2020-04-22T17:13:15Z","abstract_excerpt":"We present a comparison of methods for uncertainty quantification (UQ) in deep learning algorithms in the context of a simple physical system. Three of the most common uncertainty quantification methods - Bayesian Neural Networks (BNN), Concrete Dropout (CD), and Deep Ensembles (DE) - are compared to the standard analytic error propagation. We discuss this comparison in terms endemic to both machine learning (\"epistemic\" and \"aleatoric\") and the physical sciences (\"statistical\" and \"systematic\"). The comparisons are presented in terms of simulated experimental measurements of a single pendulum"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.10710","kind":"arxiv","version":3},"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/2004.10710/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":"2004.10710","created_at":"2026-07-05T01:57:16.346838+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.10710v3","created_at":"2026-07-05T01:57:16.346838+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.10710","created_at":"2026-07-05T01:57:16.346838+00:00"},{"alias_kind":"pith_short_12","alias_value":"MVPMJFKNRAWM","created_at":"2026-07-05T01:57:16.346838+00:00"},{"alias_kind":"pith_short_16","alias_value":"MVPMJFKNRAWMYPIL","created_at":"2026-07-05T01:57:16.346838+00:00"},{"alias_kind":"pith_short_8","alias_value":"MVPMJFKN","created_at":"2026-07-05T01:57:16.346838+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.18066","citing_title":"Towards Uncertainty Aware Task Delegation and Human-AI Collaborative Decision-Making","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MVPMJFKNRAWMYPILCKF4O4SUFD","json":"https://pith.science/pith/MVPMJFKNRAWMYPILCKF4O4SUFD.json","graph_json":"https://pith.science/api/pith-number/MVPMJFKNRAWMYPILCKF4O4SUFD/graph.json","events_json":"https://pith.science/api/pith-number/MVPMJFKNRAWMYPILCKF4O4SUFD/events.json","paper":"https://pith.science/paper/MVPMJFKN"},"agent_actions":{"view_html":"https://pith.science/pith/MVPMJFKNRAWMYPILCKF4O4SUFD","download_json":"https://pith.science/pith/MVPMJFKNRAWMYPILCKF4O4SUFD.json","view_paper":"https://pith.science/paper/MVPMJFKN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.10710&json=true","fetch_graph":"https://pith.science/api/pith-number/MVPMJFKNRAWMYPILCKF4O4SUFD/graph.json","fetch_events":"https://pith.science/api/pith-number/MVPMJFKNRAWMYPILCKF4O4SUFD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MVPMJFKNRAWMYPILCKF4O4SUFD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MVPMJFKNRAWMYPILCKF4O4SUFD/action/storage_attestation","attest_author":"https://pith.science/pith/MVPMJFKNRAWMYPILCKF4O4SUFD/action/author_attestation","sign_citation":"https://pith.science/pith/MVPMJFKNRAWMYPILCKF4O4SUFD/action/citation_signature","submit_replication":"https://pith.science/pith/MVPMJFKNRAWMYPILCKF4O4SUFD/action/replication_record"}},"created_at":"2026-07-05T01:57:16.346838+00:00","updated_at":"2026-07-05T01:57:16.346838+00:00"}