{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:7TPPYHMXIYUYAFFQTN2G7BRIEK","short_pith_number":"pith:7TPPYHMX","canonical_record":{"source":{"id":"2306.07158","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-06-12T14:44:22Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"0d96fa366564f42e93582445eed6f6f3960237ffbdc815c8f71aff30096e8fa3","abstract_canon_sha256":"d24c60442ee79d419f66a93e817e22b3fe402c40d86e548bf815a2b8195cfc8d"},"schema_version":"1.0"},"canonical_sha256":"fcdefc1d9746298014b09b746f8628228efde89e5689bfe2d6bbacad141a7a6a","source":{"kind":"arxiv","id":"2306.07158","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.07158","created_at":"2026-07-05T06:19:48Z"},{"alias_kind":"arxiv_version","alias_value":"2306.07158v1","created_at":"2026-07-05T06:19:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.07158","created_at":"2026-07-05T06:19:48Z"},{"alias_kind":"pith_short_12","alias_value":"7TPPYHMXIYUY","created_at":"2026-07-05T06:19:48Z"},{"alias_kind":"pith_short_16","alias_value":"7TPPYHMXIYUYAFFQ","created_at":"2026-07-05T06:19:48Z"},{"alias_kind":"pith_short_8","alias_value":"7TPPYHMX","created_at":"2026-07-05T06:19:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:7TPPYHMXIYUYAFFQTN2G7BRIEK","target":"record","payload":{"canonical_record":{"source":{"id":"2306.07158","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-06-12T14:44:22Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"0d96fa366564f42e93582445eed6f6f3960237ffbdc815c8f71aff30096e8fa3","abstract_canon_sha256":"d24c60442ee79d419f66a93e817e22b3fe402c40d86e548bf815a2b8195cfc8d"},"schema_version":"1.0"},"canonical_sha256":"fcdefc1d9746298014b09b746f8628228efde89e5689bfe2d6bbacad141a7a6a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:19:48.453717Z","signature_b64":"JkDAP103Gm9y/56pEdfmFouytNVDMW39EZIBm7eUEEFftG7kwQU2m8HAQfwGXh+bsTysHlnY4353dhZrmCcNCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fcdefc1d9746298014b09b746f8628228efde89e5689bfe2d6bbacad141a7a6a","last_reissued_at":"2026-07-05T06:19:48.453182Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:19:48.453182Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.07158","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":"2026-07-05T06:19:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PRY4IL3U5IfYzLSjSXd9tWwz462gC52gFq+hDjCSvoiAjAi0OhoEfFw9BMAYjKS4M9R3fmQNzvrfZ5fTh7QSAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T20:54:28.999747Z"},"content_sha256":"0d872c6a50a89bbd1b42466bcc199c6e8b5025120d4b47fae7b44a1b455be848","schema_version":"1.0","event_id":"sha256:0d872c6a50a89bbd1b42466bcc199c6e8b5025120d4b47fae7b44a1b455be848"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:7TPPYHMXIYUYAFFQTN2G7BRIEK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Riemannian Laplace approximations for Bayesian neural networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Federico Bergamin, Georgios Arvanitidis, Pablo Moreno-Mu\\~noz, S{\\o}ren Hauberg","submitted_at":"2023-06-12T14:44:22Z","abstract_excerpt":"Bayesian neural networks often approximate the weight-posterior with a Gaussian distribution. However, practical posteriors are often, even locally, highly non-Gaussian, and empirical performance deteriorates. We propose a simple parametric approximate posterior that adapts to the shape of the true posterior through a Riemannian metric that is determined by the log-posterior gradient. We develop a Riemannian Laplace approximation where samples naturally fall into weight-regions with low negative log-posterior. We show that these samples can be drawn by solving a system of ordinary differential"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.07158","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/2306.07158/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":"2026-07-05T06:19:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xTsC6C/fF7cr+qc702FuRIpcFEtiVHbB2YEmZDHA99Qpga5uPS7y06a+zWHyCAGjELRuKKUHIED2Li3FfEd8Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T20:54:29.000120Z"},"content_sha256":"0b10d58d138c109d2db398a3deceda787f76af6a778ed8135e116070ca51a1a4","schema_version":"1.0","event_id":"sha256:0b10d58d138c109d2db398a3deceda787f76af6a778ed8135e116070ca51a1a4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7TPPYHMXIYUYAFFQTN2G7BRIEK/bundle.json","state_url":"https://pith.science/pith/7TPPYHMXIYUYAFFQTN2G7BRIEK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7TPPYHMXIYUYAFFQTN2G7BRIEK/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":"2026-07-10T20:54:29Z","links":{"resolver":"https://pith.science/pith/7TPPYHMXIYUYAFFQTN2G7BRIEK","bundle":"https://pith.science/pith/7TPPYHMXIYUYAFFQTN2G7BRIEK/bundle.json","state":"https://pith.science/pith/7TPPYHMXIYUYAFFQTN2G7BRIEK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7TPPYHMXIYUYAFFQTN2G7BRIEK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:7TPPYHMXIYUYAFFQTN2G7BRIEK","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":"d24c60442ee79d419f66a93e817e22b3fe402c40d86e548bf815a2b8195cfc8d","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-06-12T14:44:22Z","title_canon_sha256":"0d96fa366564f42e93582445eed6f6f3960237ffbdc815c8f71aff30096e8fa3"},"schema_version":"1.0","source":{"id":"2306.07158","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.07158","created_at":"2026-07-05T06:19:48Z"},{"alias_kind":"arxiv_version","alias_value":"2306.07158v1","created_at":"2026-07-05T06:19:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.07158","created_at":"2026-07-05T06:19:48Z"},{"alias_kind":"pith_short_12","alias_value":"7TPPYHMXIYUY","created_at":"2026-07-05T06:19:48Z"},{"alias_kind":"pith_short_16","alias_value":"7TPPYHMXIYUYAFFQ","created_at":"2026-07-05T06:19:48Z"},{"alias_kind":"pith_short_8","alias_value":"7TPPYHMX","created_at":"2026-07-05T06:19:48Z"}],"graph_snapshots":[{"event_id":"sha256:0b10d58d138c109d2db398a3deceda787f76af6a778ed8135e116070ca51a1a4","target":"graph","created_at":"2026-07-05T06:19:48Z","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/2306.07158/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian neural networks often approximate the weight-posterior with a Gaussian distribution. However, practical posteriors are often, even locally, highly non-Gaussian, and empirical performance deteriorates. We propose a simple parametric approximate posterior that adapts to the shape of the true posterior through a Riemannian metric that is determined by the log-posterior gradient. We develop a Riemannian Laplace approximation where samples naturally fall into weight-regions with low negative log-posterior. We show that these samples can be drawn by solving a system of ordinary differential","authors_text":"Federico Bergamin, Georgios Arvanitidis, Pablo Moreno-Mu\\~noz, S{\\o}ren Hauberg","cross_cats":["cs.LG","stat.ME"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-06-12T14:44:22Z","title":"Riemannian Laplace approximations for Bayesian neural networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.07158","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":"sha256:0d872c6a50a89bbd1b42466bcc199c6e8b5025120d4b47fae7b44a1b455be848","target":"record","created_at":"2026-07-05T06:19:48Z","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":"d24c60442ee79d419f66a93e817e22b3fe402c40d86e548bf815a2b8195cfc8d","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-06-12T14:44:22Z","title_canon_sha256":"0d96fa366564f42e93582445eed6f6f3960237ffbdc815c8f71aff30096e8fa3"},"schema_version":"1.0","source":{"id":"2306.07158","kind":"arxiv","version":1}},"canonical_sha256":"fcdefc1d9746298014b09b746f8628228efde89e5689bfe2d6bbacad141a7a6a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fcdefc1d9746298014b09b746f8628228efde89e5689bfe2d6bbacad141a7a6a","first_computed_at":"2026-07-05T06:19:48.453182Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:19:48.453182Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JkDAP103Gm9y/56pEdfmFouytNVDMW39EZIBm7eUEEFftG7kwQU2m8HAQfwGXh+bsTysHlnY4353dhZrmCcNCw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:19:48.453717Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.07158","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0d872c6a50a89bbd1b42466bcc199c6e8b5025120d4b47fae7b44a1b455be848","sha256:0b10d58d138c109d2db398a3deceda787f76af6a778ed8135e116070ca51a1a4"],"state_sha256":"b810d5ec62a2e43614a5b86acadbb8010809197330de302e618d9ab8f5287508"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rxfVAZYPqh5vN2KyGd9xjHHL5M1HzSTmRVo8KJZr5RGVg/XvEwQBPahf8vRxPe96wMC+AwiDIlIMEvCCG8WFDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T20:54:29.002300Z","bundle_sha256":"7db65468fc940c1fc2933b3af18b3775d3c467461393dc79e2f7cce4a08a1b39"}}