{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:YBJN77WGQMX5HBZ2IMAWSSSXIJ","short_pith_number":"pith:YBJN77WG","canonical_record":{"source":{"id":"2106.14806","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-28T15:30:40Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"08e558bdf5264296f993d10f8a1c0763916691abb3c5b03c7eef5b8ba054a99f","abstract_canon_sha256":"5083c3e5515d82b2104d8633abeb38ca62213bfd5322ebfad34aaeeec5e0e24b"},"schema_version":"1.0"},"canonical_sha256":"c052dffec6832fd3873a4301694a5742696f7ab2c7114389b93bdd447e40bb0b","source":{"kind":"arxiv","id":"2106.14806","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.14806","created_at":"2026-07-05T04:04:39Z"},{"alias_kind":"arxiv_version","alias_value":"2106.14806v3","created_at":"2026-07-05T04:04:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.14806","created_at":"2026-07-05T04:04:39Z"},{"alias_kind":"pith_short_12","alias_value":"YBJN77WGQMX5","created_at":"2026-07-05T04:04:39Z"},{"alias_kind":"pith_short_16","alias_value":"YBJN77WGQMX5HBZ2","created_at":"2026-07-05T04:04:39Z"},{"alias_kind":"pith_short_8","alias_value":"YBJN77WG","created_at":"2026-07-05T04:04:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:YBJN77WGQMX5HBZ2IMAWSSSXIJ","target":"record","payload":{"canonical_record":{"source":{"id":"2106.14806","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-28T15:30:40Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"08e558bdf5264296f993d10f8a1c0763916691abb3c5b03c7eef5b8ba054a99f","abstract_canon_sha256":"5083c3e5515d82b2104d8633abeb38ca62213bfd5322ebfad34aaeeec5e0e24b"},"schema_version":"1.0"},"canonical_sha256":"c052dffec6832fd3873a4301694a5742696f7ab2c7114389b93bdd447e40bb0b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:04:39.943394Z","signature_b64":"tDXVSvzAkxLv84oCnSLF7MZ2PAsuwzIOevGuOltLTCyvvpLDnoXBrwmNcWfh5T0lHQHk/KVP3mp6eMLHPzw+Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c052dffec6832fd3873a4301694a5742696f7ab2c7114389b93bdd447e40bb0b","last_reissued_at":"2026-07-05T04:04:39.942874Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:04:39.942874Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.14806","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":"2026-07-05T04:04:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7w/4YSUFbvGBb6853G3SylN+7Ys0JUEgo+6rQIeJ8iLV9SoZ+ZMVd7mEdZdSBkXy1yZflXZa9JpkZT2afPl4Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T08:04:49.939124Z"},"content_sha256":"dcd3a3e5cea439d39ed8a1d172d6b1508673548d92609bc5f1428fe72563d3a7","schema_version":"1.0","event_id":"sha256:dcd3a3e5cea439d39ed8a1d172d6b1508673548d92609bc5f1428fe72563d3a7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:YBJN77WGQMX5HBZ2IMAWSSSXIJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Laplace Redux -- Effortless Bayesian Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Agustinus Kristiadi, Alexander Immer, Erik Daxberger, Matthias Bauer, Philipp Hennig, Runa Eschenhagen","submitted_at":"2021-06-28T15:30:40Z","abstract_excerpt":"Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty quantification and model selection. The Laplace approximation (LA) is a classic, and arguably the simplest family of approximations for the intractable posteriors of deep neural networks. Yet, despite its simplicity, the LA is not as popular as alternatives like variational Bayes or deep ensembles. This may be due to assumptions that the LA is expensive due to the involved Hessian computation, that it is difficult to i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.14806","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/2106.14806/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":"2026-07-05T04:04:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iIu25dRznoJI24ILywPtEhKiFijhYz73ogR899yjWqyVla8u36LBhhbMU2oYkzfSlx+mzvBA+qPIkn8B52IKCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T08:04:49.939514Z"},"content_sha256":"d6342d63239cfbf89c443d0dd83994850d3ee17e9c98befe8829b10b2670529b","schema_version":"1.0","event_id":"sha256:d6342d63239cfbf89c443d0dd83994850d3ee17e9c98befe8829b10b2670529b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YBJN77WGQMX5HBZ2IMAWSSSXIJ/bundle.json","state_url":"https://pith.science/pith/YBJN77WGQMX5HBZ2IMAWSSSXIJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YBJN77WGQMX5HBZ2IMAWSSSXIJ/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":"2026-07-24T08:04:49Z","links":{"resolver":"https://pith.science/pith/YBJN77WGQMX5HBZ2IMAWSSSXIJ","bundle":"https://pith.science/pith/YBJN77WGQMX5HBZ2IMAWSSSXIJ/bundle.json","state":"https://pith.science/pith/YBJN77WGQMX5HBZ2IMAWSSSXIJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YBJN77WGQMX5HBZ2IMAWSSSXIJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:YBJN77WGQMX5HBZ2IMAWSSSXIJ","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":"5083c3e5515d82b2104d8633abeb38ca62213bfd5322ebfad34aaeeec5e0e24b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-28T15:30:40Z","title_canon_sha256":"08e558bdf5264296f993d10f8a1c0763916691abb3c5b03c7eef5b8ba054a99f"},"schema_version":"1.0","source":{"id":"2106.14806","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.14806","created_at":"2026-07-05T04:04:39Z"},{"alias_kind":"arxiv_version","alias_value":"2106.14806v3","created_at":"2026-07-05T04:04:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.14806","created_at":"2026-07-05T04:04:39Z"},{"alias_kind":"pith_short_12","alias_value":"YBJN77WGQMX5","created_at":"2026-07-05T04:04:39Z"},{"alias_kind":"pith_short_16","alias_value":"YBJN77WGQMX5HBZ2","created_at":"2026-07-05T04:04:39Z"},{"alias_kind":"pith_short_8","alias_value":"YBJN77WG","created_at":"2026-07-05T04:04:39Z"}],"graph_snapshots":[{"event_id":"sha256:d6342d63239cfbf89c443d0dd83994850d3ee17e9c98befe8829b10b2670529b","target":"graph","created_at":"2026-07-05T04:04:39Z","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/2106.14806/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty quantification and model selection. The Laplace approximation (LA) is a classic, and arguably the simplest family of approximations for the intractable posteriors of deep neural networks. Yet, despite its simplicity, the LA is not as popular as alternatives like variational Bayes or deep ensembles. This may be due to assumptions that the LA is expensive due to the involved Hessian computation, that it is difficult to i","authors_text":"Agustinus Kristiadi, Alexander Immer, Erik Daxberger, Matthias Bauer, Philipp Hennig, Runa Eschenhagen","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-28T15:30:40Z","title":"Laplace Redux -- Effortless Bayesian Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.14806","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:dcd3a3e5cea439d39ed8a1d172d6b1508673548d92609bc5f1428fe72563d3a7","target":"record","created_at":"2026-07-05T04:04:39Z","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":"5083c3e5515d82b2104d8633abeb38ca62213bfd5322ebfad34aaeeec5e0e24b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-28T15:30:40Z","title_canon_sha256":"08e558bdf5264296f993d10f8a1c0763916691abb3c5b03c7eef5b8ba054a99f"},"schema_version":"1.0","source":{"id":"2106.14806","kind":"arxiv","version":3}},"canonical_sha256":"c052dffec6832fd3873a4301694a5742696f7ab2c7114389b93bdd447e40bb0b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c052dffec6832fd3873a4301694a5742696f7ab2c7114389b93bdd447e40bb0b","first_computed_at":"2026-07-05T04:04:39.942874Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:04:39.942874Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tDXVSvzAkxLv84oCnSLF7MZ2PAsuwzIOevGuOltLTCyvvpLDnoXBrwmNcWfh5T0lHQHk/KVP3mp6eMLHPzw+Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T04:04:39.943394Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.14806","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dcd3a3e5cea439d39ed8a1d172d6b1508673548d92609bc5f1428fe72563d3a7","sha256:d6342d63239cfbf89c443d0dd83994850d3ee17e9c98befe8829b10b2670529b"],"state_sha256":"2fdc3c86c48ef27aa6332fd31c8b3ad30f1c6afc4203b855c4589d11dba97611"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pZnYWxQO5NT8HAwjKkoxmnO5PpjVIPFewEBqTJMRCC4Edn+uglBwOG4nlyOF38N9jgPpW4wC+0N7LuTMfbryAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-24T08:04:49.944324Z","bundle_sha256":"3fb0134899c2e10569c78e87f8ddea3d99ef0c2c2d00e37440428570907940ec"}}