{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:GUBLO43XQNZWCRMS2EYYOZLIFS","short_pith_number":"pith:GUBLO43X","canonical_record":{"source":{"id":"2607.28248","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-07-30T14:10:31Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"96aefadc87f235e8e134240791ffb1ff5ddbff9fb16cbbfe789811c3cbbf9be5","abstract_canon_sha256":"6c12e04fe98cd0c57595b5cd7492f939ffc40bdcefb1543ce9a46a9eb9799c75"},"schema_version":"1.0"},"canonical_sha256":"3502b773778373614592d1318765682cb26765d4bb8bdf26dc0d224c5bedda03","source":{"kind":"arxiv","id":"2607.28248","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.28248","created_at":"2026-07-31T01:36:54Z"},{"alias_kind":"arxiv_version","alias_value":"2607.28248v1","created_at":"2026-07-31T01:36:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.28248","created_at":"2026-07-31T01:36:54Z"},{"alias_kind":"pith_short_12","alias_value":"GUBLO43XQNZW","created_at":"2026-07-31T01:36:54Z"},{"alias_kind":"pith_short_16","alias_value":"GUBLO43XQNZWCRMS","created_at":"2026-07-31T01:36:54Z"},{"alias_kind":"pith_short_8","alias_value":"GUBLO43X","created_at":"2026-07-31T01:36:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:GUBLO43XQNZWCRMS2EYYOZLIFS","target":"record","payload":{"canonical_record":{"source":{"id":"2607.28248","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-07-30T14:10:31Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"96aefadc87f235e8e134240791ffb1ff5ddbff9fb16cbbfe789811c3cbbf9be5","abstract_canon_sha256":"6c12e04fe98cd0c57595b5cd7492f939ffc40bdcefb1543ce9a46a9eb9799c75"},"schema_version":"1.0"},"canonical_sha256":"3502b773778373614592d1318765682cb26765d4bb8bdf26dc0d224c5bedda03","receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3502b773778373614592d1318765682cb26765d4bb8bdf26dc0d224c5bedda03","last_reissued_at":"2026-07-31T01:36:54.938358Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T01:36:54.938358Z"},"source_kind":"arxiv","source_id":"2607.28248","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T01:36:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SL2cVZGJ0RvF3VElsn9zCTIxCAM3926XDotavCWufKbAzRIpG2LCN1q1KNpJIUxzrMro3X2GMOoGsEaLElhZBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T14:28:59.398186Z"},"content_sha256":"0d4db8824cbeed429467675a398f3782e780e7c340f369d356c124b86ca75c1c","schema_version":"1.0","event_id":"sha256:0d4db8824cbeed429467675a398f3782e780e7c340f369d356c124b86ca75c1c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:GUBLO43XQNZWCRMS2EYYOZLIFS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Uncertainty quantification for trustworthy deep learning: Methods and measures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"H. Martin Gillis, Thomas Trappenberg","submitted_at":"2026-07-30T14:10:31Z","abstract_excerpt":"The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures lack principled uncertainty quantification. This survey provides a structured, critical review of methods for Uncertainty Quantification (UQ) in deep learning, scoped to ensemble-based and approximate Bayesian approaches and the measures used to summarize their outputs. Relative to existing UQ surveys, our contribution is depth on efficient ensemble approximations and single-pass methods, and a unified treatment that separates the method producin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.28248","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/2607.28248/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":"2026-07-31T01:36:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bPH1bEAvRHI43Z6KvBWmxrXINILsAPNkDCXgQFjGofS2MqSbh/KTdDiso9SX3BWhnGd+sQgu3HQvukncfu8YDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T14:28:59.399169Z"},"content_sha256":"52d9a6fd1afdd17f95fcff340dfad4d6aca266b713333169238c05d5d77efcdd","schema_version":"1.0","event_id":"sha256:52d9a6fd1afdd17f95fcff340dfad4d6aca266b713333169238c05d5d77efcdd"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:GUBLO43XQNZWCRMS2EYYOZLIFS","target":"integrity","payload":{"note":"Identifier '10.48550/arxiv.2201' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"URL:https://raw.githubusercontent.com/ mlresearch/v286/main/assets/schweighofer25a/ schweighofer25a.pdf. Workshop: Mathematics of modern machine learning. Schweighofer, K., Aichberger, L., Ielanskyi, M., Klambauer, G., Hochreiter, S., 2023b","arxiv_id":"2607.28248","detector":"doi_compliance","evidence":{"doi":"10.48550/arxiv.2201","arxiv_id":null,"ref_index":3640,"raw_excerpt":"URL:https://raw.githubusercontent.com/ mlresearch/v286/main/assets/schweighofer25a/ schweighofer25a.pdf. Workshop: Mathematics of modern machine learning. Schweighofer, K., Aichberger, L., Ielanskyi, M., Klambauer, G., Hochreiter, S., 2023b. Quantification of uncertainty with adversarial models, in: Neural Information Processing Sys- tems (NeurIPS), pp. 1–39. URL:https://openreview. net/pdf?id=5eu","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":3640,"audited_at":"2026-07-31T13:31:40.154501Z","event_type":"pith.integrity.v1","detected_doi":"10.48550/arxiv.2201","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"32ffa7a5087fd3100a74138470d3b4db76536b35115ef0c0b1fb5f6653003ef5","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":14404,"payload_sha256":"c76ceb93893da55aa47a8c56f4662cf1d53f618952ea7fe4803747fc323b863e","signature_b64":"pIh32bwNERGFdiiOauXQzlsoWsTbLoF2mvQKx0AfKXf7FK1HgutOo15WutjmtF7MaePOwk/EOC11yaNrveakAQ==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T13:36:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"REYA/sY+Wc1hsk5ex10CxzUu6rg+VqxdGowUuAlh6vx3KbKBb6hG79GH6qroOw1tVfqlOxIl+esJumqQ5UG7DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T14:28:59.435281Z"},"content_sha256":"a804d1be5b95f577ce475125654bd3fbd89819f1a43508262edb245f57efd4a4","schema_version":"1.0","event_id":"sha256:a804d1be5b95f577ce475125654bd3fbd89819f1a43508262edb245f57efd4a4"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:GUBLO43XQNZWCRMS2EYYOZLIFS","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1007/11564096_28) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Active learning for probability estimation using Jensen-Shannon divergence, in: European Conference on Machine Learning, pp. 268–279. doi:10.1007/11564096_ 28. Mena, J., Pujol, O., Vitrià, J., 2021. A survey on uncertainty estimation in dee","arxiv_id":"2607.28248","detector":"doi_compliance","evidence":{"ref_index":2005,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.1007/11564096_","reconstructed_doi":"10.1007/11564096_28"},"severity":"advisory","ref_index":2005,"audited_at":"2026-07-31T13:31:40.154501Z","event_type":"pith.integrity.v1","detected_doi":"10.1007/11564096_28","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"124a394affaf4961f0aa68c54626d932a4f746264a1aa1189c17d5b4e780d945","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":14403,"payload_sha256":"1acf434905a2337de54661508e5d82c31b338f7b41443a5a12951ced8e36b46b","signature_b64":"SLFbwqlCDpcS7vUbCo4J5D+OjTogRePIfwbsumnkwhGZG71+KkyistATTQpLBc2BIihyD/Kxa6A0Knc6M/oYAQ==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T13:36:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/iIv3Z5r/T3V69umxyzusa7fN0j2wCSCReobO6llvtUq/7JvKJOpU8JTgO7XFWAZjWMGusd9sHCOFV1oTXtcDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T14:28:59.436027Z"},"content_sha256":"02f01f9ca100e4a131cb9ca32fd95410cd788d6d08d7be463d214016882d53ee","schema_version":"1.0","event_id":"sha256:02f01f9ca100e4a131cb9ca32fd95410cd788d6d08d7be463d214016882d53ee"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GUBLO43XQNZWCRMS2EYYOZLIFS/bundle.json","state_url":"https://pith.science/pith/GUBLO43XQNZWCRMS2EYYOZLIFS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GUBLO43XQNZWCRMS2EYYOZLIFS/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":"2026-08-04T14:28:59Z","links":{"resolver":"https://pith.science/pith/GUBLO43XQNZWCRMS2EYYOZLIFS","bundle":"https://pith.science/pith/GUBLO43XQNZWCRMS2EYYOZLIFS/bundle.json","state":"https://pith.science/pith/GUBLO43XQNZWCRMS2EYYOZLIFS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GUBLO43XQNZWCRMS2EYYOZLIFS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:GUBLO43XQNZWCRMS2EYYOZLIFS","merge_version":"pith-open-graph-merge-v1","event_count":4,"valid_event_count":4,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"6c12e04fe98cd0c57595b5cd7492f939ffc40bdcefb1543ce9a46a9eb9799c75","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-07-30T14:10:31Z","title_canon_sha256":"96aefadc87f235e8e134240791ffb1ff5ddbff9fb16cbbfe789811c3cbbf9be5"},"schema_version":"1.0","source":{"id":"2607.28248","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.28248","created_at":"2026-07-31T01:36:54Z"},{"alias_kind":"arxiv_version","alias_value":"2607.28248v1","created_at":"2026-07-31T01:36:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.28248","created_at":"2026-07-31T01:36:54Z"},{"alias_kind":"pith_short_12","alias_value":"GUBLO43XQNZW","created_at":"2026-07-31T01:36:54Z"},{"alias_kind":"pith_short_16","alias_value":"GUBLO43XQNZWCRMS","created_at":"2026-07-31T01:36:54Z"},{"alias_kind":"pith_short_8","alias_value":"GUBLO43X","created_at":"2026-07-31T01:36:54Z"}],"graph_snapshots":[{"event_id":"sha256:52d9a6fd1afdd17f95fcff340dfad4d6aca266b713333169238c05d5d77efcdd","target":"graph","created_at":"2026-07-31T01:36:54Z","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/2607.28248/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures lack principled uncertainty quantification. This survey provides a structured, critical review of methods for Uncertainty Quantification (UQ) in deep learning, scoped to ensemble-based and approximate Bayesian approaches and the measures used to summarize their outputs. Relative to existing UQ surveys, our contribution is depth on efficient ensemble approximations and single-pass methods, and a unified treatment that separates the method producin","authors_text":"H. Martin Gillis, Thomas Trappenberg","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-07-30T14:10:31Z","title":"Uncertainty quantification for trustworthy deep learning: Methods and measures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.28248","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":"sha256:0d4db8824cbeed429467675a398f3782e780e7c340f369d356c124b86ca75c1c","target":"record","created_at":"2026-07-31T01:36:54Z","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":"6c12e04fe98cd0c57595b5cd7492f939ffc40bdcefb1543ce9a46a9eb9799c75","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-07-30T14:10:31Z","title_canon_sha256":"96aefadc87f235e8e134240791ffb1ff5ddbff9fb16cbbfe789811c3cbbf9be5"},"schema_version":"1.0","source":{"id":"2607.28248","kind":"arxiv","version":1}},"canonical_sha256":"3502b773778373614592d1318765682cb26765d4bb8bdf26dc0d224c5bedda03","receipt":{"builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3502b773778373614592d1318765682cb26765d4bb8bdf26dc0d224c5bedda03","first_computed_at":"2026-07-31T01:36:54.938358Z","kind":"pith_receipt","last_reissued_at":"2026-07-31T01:36:54.938358Z","receipt_version":"0.3","signature_status":"unsigned_v0"},"source_id":"2607.28248","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:02f01f9ca100e4a131cb9ca32fd95410cd788d6d08d7be463d214016882d53ee","sha256:a804d1be5b95f577ce475125654bd3fbd89819f1a43508262edb245f57efd4a4"]}],"invalid_events":[],"applied_event_ids":["sha256:0d4db8824cbeed429467675a398f3782e780e7c340f369d356c124b86ca75c1c","sha256:52d9a6fd1afdd17f95fcff340dfad4d6aca266b713333169238c05d5d77efcdd"],"state_sha256":"5cac32a18eb2bc7e5b33e3a9ee4ffe63fc853fad5e02da55a57557e941b05331"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Pwd1NNjWM21C2bc7I+R5r0iJrSroraYqC9gjPZ7QCWwQjq8+/R7Lj68fw+ZdAZN+FCmpNXGmCj8K2OubWP22Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T14:28:59.441205Z","bundle_sha256":"7ae8e0ac04a1e3de65ac5184cf29a0ec59ab31e811809c2aaba036fd7343a6ca"}}