{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:C2HFGXCX44ASWVRFPMUFUAJ2NK","short_pith_number":"pith:C2HFGXCX","canonical_record":{"source":{"id":"2402.00809","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-01T17:45:26Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"39674c2e6a04f100c20a50cd0876112820a606f2cb2f12d5a3256b124a961012","abstract_canon_sha256":"df220b3fd6e48e96e65a09fffd7d754b397a70200dd1a300bd42fe2f01ebcdbd"},"schema_version":"1.0"},"canonical_sha256":"168e535c57e7012b56257b285a013a6aa526d9ab682336fefbfd8087bea53c3f","source":{"kind":"arxiv","id":"2402.00809","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.00809","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"arxiv_version","alias_value":"2402.00809v5","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.00809","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"pith_short_12","alias_value":"C2HFGXCX44AS","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"pith_short_16","alias_value":"C2HFGXCX44ASWVRF","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"pith_short_8","alias_value":"C2HFGXCX","created_at":"2026-07-05T08:52:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:C2HFGXCX44ASWVRFPMUFUAJ2NK","target":"record","payload":{"canonical_record":{"source":{"id":"2402.00809","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-01T17:45:26Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"39674c2e6a04f100c20a50cd0876112820a606f2cb2f12d5a3256b124a961012","abstract_canon_sha256":"df220b3fd6e48e96e65a09fffd7d754b397a70200dd1a300bd42fe2f01ebcdbd"},"schema_version":"1.0"},"canonical_sha256":"168e535c57e7012b56257b285a013a6aa526d9ab682336fefbfd8087bea53c3f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:52:28.921270Z","signature_b64":"CBaZDerCtD+AF++jTFPquFLu3pTR578jGSmes8/h4RVTre2ttLhK6YrM0/U1SLEKZiTBu7G1xlzl9lf6BgW+Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"168e535c57e7012b56257b285a013a6aa526d9ab682336fefbfd8087bea53c3f","last_reissued_at":"2026-07-05T08:52:28.920846Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:52:28.920846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.00809","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:52:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lYqxL7npplOUSZQFX+BRSzjeAvbj697xfasFUpnzEE9QYoPUUSUadjSaATEA6pFXZWG759I7Hdzrjmc9X5NjCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-25T16:53:01.166817Z"},"content_sha256":"7120c22b0d590dd3cb7488d03af922b10854797934353615badc5ecdb030f1f8","schema_version":"1.0","event_id":"sha256:7120c22b0d590dd3cb7488d03af922b10854797934353615badc5ecdb030f1f8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:C2HFGXCX44ASWVRFPMUFUAJ2NK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Agustinus Kristiadi, Alexander Immer, Aliaksandr Hubin, Andrew Gordon Wilson, Christopher Nemeth, David Dunson, David R\\\"ugamer, Jos\\'e Miguel Hern\\'andez-Lobato, Julyan Arbel, Konstantina Palla, Laurence Aitchison, Maria Skoularidou, Maurizio Filippone, Max Welling, Michael A. Osborne, Mohammad Emtiyaz Khan, Philipp Hennig, Ruqi Zhang, Stephan Mandt, Theodore Papamarkou, Theofanis Karaletsos, Tim G. J. Rudner, Vincent Fortuin, Yee Whye Teh, Yingzhen Li","submitted_at":"2024-02-01T17:45:26Z","abstract_excerpt":"In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.00809","kind":"arxiv","version":5},"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/2402.00809/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":"2026-07-05T08:52:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zNk9zaVmWxCAJtnABKvyrBLJWOa8ZUew/CG2ZgDTziR9GEq6GhfK52YgQwguCmksBAHI911LC27Pw4VIkNegDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-25T16:53:01.167224Z"},"content_sha256":"fca808e815c81906a3025a7588bc7e9696053e6a544967a4e8bb4bc5743edf3f","schema_version":"1.0","event_id":"sha256:fca808e815c81906a3025a7588bc7e9696053e6a544967a4e8bb4bc5743edf3f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C2HFGXCX44ASWVRFPMUFUAJ2NK/bundle.json","state_url":"https://pith.science/pith/C2HFGXCX44ASWVRFPMUFUAJ2NK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C2HFGXCX44ASWVRFPMUFUAJ2NK/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":"2026-07-25T16:53:01Z","links":{"resolver":"https://pith.science/pith/C2HFGXCX44ASWVRFPMUFUAJ2NK","bundle":"https://pith.science/pith/C2HFGXCX44ASWVRFPMUFUAJ2NK/bundle.json","state":"https://pith.science/pith/C2HFGXCX44ASWVRFPMUFUAJ2NK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C2HFGXCX44ASWVRFPMUFUAJ2NK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:C2HFGXCX44ASWVRFPMUFUAJ2NK","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":"df220b3fd6e48e96e65a09fffd7d754b397a70200dd1a300bd42fe2f01ebcdbd","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-01T17:45:26Z","title_canon_sha256":"39674c2e6a04f100c20a50cd0876112820a606f2cb2f12d5a3256b124a961012"},"schema_version":"1.0","source":{"id":"2402.00809","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.00809","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"arxiv_version","alias_value":"2402.00809v5","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.00809","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"pith_short_12","alias_value":"C2HFGXCX44AS","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"pith_short_16","alias_value":"C2HFGXCX44ASWVRF","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"pith_short_8","alias_value":"C2HFGXCX","created_at":"2026-07-05T08:52:28Z"}],"graph_snapshots":[{"event_id":"sha256:fca808e815c81906a3025a7588bc7e9696053e6a544967a4e8bb4bc5743edf3f","target":"graph","created_at":"2026-07-05T08:52:28Z","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/2402.00809/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, ","authors_text":"Agustinus Kristiadi, Alexander Immer, Aliaksandr Hubin, Andrew Gordon Wilson, Christopher Nemeth, David Dunson, David R\\\"ugamer, Jos\\'e Miguel Hern\\'andez-Lobato, Julyan Arbel, Konstantina Palla, Laurence Aitchison, Maria Skoularidou, Maurizio Filippone, Max Welling, Michael A. Osborne, Mohammad Emtiyaz Khan, Philipp Hennig, Ruqi Zhang, Stephan Mandt, Theodore Papamarkou, Theofanis Karaletsos, Tim G. J. Rudner, Vincent Fortuin, Yee Whye Teh, Yingzhen Li","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-01T17:45:26Z","title":"Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.00809","kind":"arxiv","version":5},"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":"sha256:7120c22b0d590dd3cb7488d03af922b10854797934353615badc5ecdb030f1f8","target":"record","created_at":"2026-07-05T08:52:28Z","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":"df220b3fd6e48e96e65a09fffd7d754b397a70200dd1a300bd42fe2f01ebcdbd","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-01T17:45:26Z","title_canon_sha256":"39674c2e6a04f100c20a50cd0876112820a606f2cb2f12d5a3256b124a961012"},"schema_version":"1.0","source":{"id":"2402.00809","kind":"arxiv","version":5}},"canonical_sha256":"168e535c57e7012b56257b285a013a6aa526d9ab682336fefbfd8087bea53c3f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"168e535c57e7012b56257b285a013a6aa526d9ab682336fefbfd8087bea53c3f","first_computed_at":"2026-07-05T08:52:28.920846Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:52:28.920846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CBaZDerCtD+AF++jTFPquFLu3pTR578jGSmes8/h4RVTre2ttLhK6YrM0/U1SLEKZiTBu7G1xlzl9lf6BgW+Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:52:28.921270Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.00809","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7120c22b0d590dd3cb7488d03af922b10854797934353615badc5ecdb030f1f8","sha256:fca808e815c81906a3025a7588bc7e9696053e6a544967a4e8bb4bc5743edf3f"],"state_sha256":"25ded0a3400935f78601f93455f041251ef6043feb7cb5db5b52137fc860025d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"32AhtS6GDGeJTAdDgHN+g0mg3hPrL5eSPzAV0wsL9l6HMhBH9NqyLhzUOuSQ9iN4olmuShmXr0d7opYIyLw8Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-25T16:53:01.169521Z","bundle_sha256":"0709a877b439cda40dfb82583d6e1d0bf1c1fb7a829777f2ba6238f73e65677f"}}