{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:SZSMG7XEKME2RFOHOHG7OIGPPU","short_pith_number":"pith:SZSMG7XE","canonical_record":{"source":{"id":"1906.00816","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-03T13:51:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"aca695a10bf091588aa79a021268b12fd363f060a94ccf9085875da365f821f9","abstract_canon_sha256":"a93d846b3e64c7f43cc86758b90d4cc08a4bae317a5ea7fd04dfe6d698a05c43"},"schema_version":"1.0"},"canonical_sha256":"9664c37ee45309a895c771cdf720cf7d22e8305415f5511b45f760616ca0a2e3","source":{"kind":"arxiv","id":"1906.00816","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.00816","created_at":"2026-07-05T02:08:27Z"},{"alias_kind":"arxiv_version","alias_value":"1906.00816v3","created_at":"2026-07-05T02:08:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.00816","created_at":"2026-07-05T02:08:27Z"},{"alias_kind":"pith_short_12","alias_value":"SZSMG7XEKME2","created_at":"2026-07-05T02:08:27Z"},{"alias_kind":"pith_short_16","alias_value":"SZSMG7XEKME2RFOH","created_at":"2026-07-05T02:08:27Z"},{"alias_kind":"pith_short_8","alias_value":"SZSMG7XE","created_at":"2026-07-05T02:08:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:SZSMG7XEKME2RFOHOHG7OIGPPU","target":"record","payload":{"canonical_record":{"source":{"id":"1906.00816","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-03T13:51:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"aca695a10bf091588aa79a021268b12fd363f060a94ccf9085875da365f821f9","abstract_canon_sha256":"a93d846b3e64c7f43cc86758b90d4cc08a4bae317a5ea7fd04dfe6d698a05c43"},"schema_version":"1.0"},"canonical_sha256":"9664c37ee45309a895c771cdf720cf7d22e8305415f5511b45f760616ca0a2e3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:08:27.532354Z","signature_b64":"fMJ+TWkMJFrKN6adR3Vio1/zvDZP8yc7T1sLe3OCylTbeWg1PIkUOxfczyJUAx27bVWkiUL7DthQDY+ip5rnCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9664c37ee45309a895c771cdf720cf7d22e8305415f5511b45f760616ca0a2e3","last_reissued_at":"2026-07-05T02:08:27.531882Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:08:27.531882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.00816","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:08:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0t6eD93f+LN30L8RoYYc/wurV0xxsULYP8xlcUDO5lTyFa9rOzz1gfM7dgU3m+cVnygRp3HKIlMZcEUDDmoAAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T05:42:44.741771Z"},"content_sha256":"90b1388641137df8fcc087082d0c5bbd39b0e8311a88566df98be5ceeae76f5b","schema_version":"1.0","event_id":"sha256:90b1388641137df8fcc087082d0c5bbd39b0e8311a88566df98be5ceeae76f5b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:SZSMG7XEKME2RFOHOHG7OIGPPU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Evidential Deep Learning with PAC Regularization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Manuel Haussmann, Melih Kandemir, Sebastian Gerwinn","submitted_at":"2019-06-03T13:51:51Z","abstract_excerpt":"We propose a novel method for closed-form predictive distribution modeling with neural nets. In quantifying prediction uncertainty, we build on Evidential Deep Learning, which has been impactful as being both simple to implement and giving closed-form access to predictive uncertainty. We employ it to model aleatoric uncertainty and extend it to account also for epistemic uncertainty by converting it to a Bayesian Neural Net. While extending its uncertainty quantification capabilities, we maintain its analytically accessible predictive distribution model by performing progressive moment matchin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.00816","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/1906.00816/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":"2026-07-05T02:08:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ncfh9+WkNj6Rpcf0KPMqnGHh71yGj/sbTiSOBbuJJ2mB+S1CG4mVYz+eQyfXbVnIDujx/HDBJ4ZB2XAwrsIOAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T05:42:44.742725Z"},"content_sha256":"7e2f2ad06a76e076f4fc31ccce245f7e45a189ba4f857e3a8a718aa17f913400","schema_version":"1.0","event_id":"sha256:7e2f2ad06a76e076f4fc31ccce245f7e45a189ba4f857e3a8a718aa17f913400"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SZSMG7XEKME2RFOHOHG7OIGPPU/bundle.json","state_url":"https://pith.science/pith/SZSMG7XEKME2RFOHOHG7OIGPPU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SZSMG7XEKME2RFOHOHG7OIGPPU/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":"2026-08-10T05:42:44Z","links":{"resolver":"https://pith.science/pith/SZSMG7XEKME2RFOHOHG7OIGPPU","bundle":"https://pith.science/pith/SZSMG7XEKME2RFOHOHG7OIGPPU/bundle.json","state":"https://pith.science/pith/SZSMG7XEKME2RFOHOHG7OIGPPU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SZSMG7XEKME2RFOHOHG7OIGPPU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:SZSMG7XEKME2RFOHOHG7OIGPPU","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":"a93d846b3e64c7f43cc86758b90d4cc08a4bae317a5ea7fd04dfe6d698a05c43","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-03T13:51:51Z","title_canon_sha256":"aca695a10bf091588aa79a021268b12fd363f060a94ccf9085875da365f821f9"},"schema_version":"1.0","source":{"id":"1906.00816","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.00816","created_at":"2026-07-05T02:08:27Z"},{"alias_kind":"arxiv_version","alias_value":"1906.00816v3","created_at":"2026-07-05T02:08:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.00816","created_at":"2026-07-05T02:08:27Z"},{"alias_kind":"pith_short_12","alias_value":"SZSMG7XEKME2","created_at":"2026-07-05T02:08:27Z"},{"alias_kind":"pith_short_16","alias_value":"SZSMG7XEKME2RFOH","created_at":"2026-07-05T02:08:27Z"},{"alias_kind":"pith_short_8","alias_value":"SZSMG7XE","created_at":"2026-07-05T02:08:27Z"}],"graph_snapshots":[{"event_id":"sha256:7e2f2ad06a76e076f4fc31ccce245f7e45a189ba4f857e3a8a718aa17f913400","target":"graph","created_at":"2026-07-05T02:08:27Z","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/1906.00816/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a novel method for closed-form predictive distribution modeling with neural nets. In quantifying prediction uncertainty, we build on Evidential Deep Learning, which has been impactful as being both simple to implement and giving closed-form access to predictive uncertainty. We employ it to model aleatoric uncertainty and extend it to account also for epistemic uncertainty by converting it to a Bayesian Neural Net. While extending its uncertainty quantification capabilities, we maintain its analytically accessible predictive distribution model by performing progressive moment matchin","authors_text":"Manuel Haussmann, Melih Kandemir, Sebastian Gerwinn","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-03T13:51:51Z","title":"Bayesian Evidential Deep Learning with PAC Regularization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.00816","kind":"arxiv","version":3},"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":"sha256:90b1388641137df8fcc087082d0c5bbd39b0e8311a88566df98be5ceeae76f5b","target":"record","created_at":"2026-07-05T02:08:27Z","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":"a93d846b3e64c7f43cc86758b90d4cc08a4bae317a5ea7fd04dfe6d698a05c43","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-03T13:51:51Z","title_canon_sha256":"aca695a10bf091588aa79a021268b12fd363f060a94ccf9085875da365f821f9"},"schema_version":"1.0","source":{"id":"1906.00816","kind":"arxiv","version":3}},"canonical_sha256":"9664c37ee45309a895c771cdf720cf7d22e8305415f5511b45f760616ca0a2e3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9664c37ee45309a895c771cdf720cf7d22e8305415f5511b45f760616ca0a2e3","first_computed_at":"2026-07-05T02:08:27.531882Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:08:27.531882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fMJ+TWkMJFrKN6adR3Vio1/zvDZP8yc7T1sLe3OCylTbeWg1PIkUOxfczyJUAx27bVWkiUL7DthQDY+ip5rnCg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:08:27.532354Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.00816","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:90b1388641137df8fcc087082d0c5bbd39b0e8311a88566df98be5ceeae76f5b","sha256:7e2f2ad06a76e076f4fc31ccce245f7e45a189ba4f857e3a8a718aa17f913400"],"state_sha256":"be7c374190dd27cd424156063b7dd232937c4a11d94398e672ab5d3d7942dcaf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CfTVnUxl/pXaZ4R4zznDqrA5mF6Uw93aADHw1c/rYcRx4ntmfa/xj3WAkP5v6E0dlad1p1+L6J9fk9Eo6uNhBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T05:42:44.748164Z","bundle_sha256":"f032942d69116039419188a59ebc28e71af895c6f6b6bbcd4647b63b103f7f10"}}