{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:SDOC44INWNHBS7VFZ6M4UEIVZD","short_pith_number":"pith:SDOC44IN","canonical_record":{"source":{"id":"2106.05251","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-09T17:46:22Z","cross_cats_sorted":["cs.CL","stat.ML"],"title_canon_sha256":"21ec2467c2e17064bdec8bacad6172babeda3de99d062d18ea7f3d93584a5283","abstract_canon_sha256":"e826ca01311760ea779017328207da1bdb2f6681ac4db6565014389df078aecd"},"schema_version":"1.0"},"canonical_sha256":"90dc2e710db34e197ea5cf99ca1115c8fc2e5925474e31794f1e6406a4ded64b","source":{"kind":"arxiv","id":"2106.05251","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.05251","created_at":"2026-07-05T02:47:54Z"},{"alias_kind":"arxiv_version","alias_value":"2106.05251v1","created_at":"2026-07-05T02:47:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.05251","created_at":"2026-07-05T02:47:54Z"},{"alias_kind":"pith_short_12","alias_value":"SDOC44INWNHB","created_at":"2026-07-05T02:47:54Z"},{"alias_kind":"pith_short_16","alias_value":"SDOC44INWNHBS7VF","created_at":"2026-07-05T02:47:54Z"},{"alias_kind":"pith_short_8","alias_value":"SDOC44IN","created_at":"2026-07-05T02:47:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:SDOC44INWNHBS7VFZ6M4UEIVZD","target":"record","payload":{"canonical_record":{"source":{"id":"2106.05251","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-09T17:46:22Z","cross_cats_sorted":["cs.CL","stat.ML"],"title_canon_sha256":"21ec2467c2e17064bdec8bacad6172babeda3de99d062d18ea7f3d93584a5283","abstract_canon_sha256":"e826ca01311760ea779017328207da1bdb2f6681ac4db6565014389df078aecd"},"schema_version":"1.0"},"canonical_sha256":"90dc2e710db34e197ea5cf99ca1115c8fc2e5925474e31794f1e6406a4ded64b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:47:54.787221Z","signature_b64":"kYos2v4fKcAKDoM2uz00CzmEHBo3PeyftvczZS2OcdisFzcs4pkWyJskgW9T28e43IvaIUg6BLMFw9UB5Cw2CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90dc2e710db34e197ea5cf99ca1115c8fc2e5925474e31794f1e6406a4ded64b","last_reissued_at":"2026-07-05T02:47:54.786732Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:47:54.786732Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.05251","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":"2026-07-05T02:47:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"owyRqVvbqJcKY2VxbP/yDm6dKy0tNqeouMf/f488VOlGGgM9E6NbFwSq6cpY6/hdzRNPktMS8+X0a//NtklEAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T18:16:11.984428Z"},"content_sha256":"2423ae255900dfe9b8f67c1231693774b198a46b24048a16855e28b9c77709c0","schema_version":"1.0","event_id":"sha256:2423ae255900dfe9b8f67c1231693774b198a46b24048a16855e28b9c77709c0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:SDOC44INWNHBS7VFZ6M4UEIVZD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Attention Belief Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bo Chen, Mingyuan Zhou, Shujian Zhang, Xinjie Fan","submitted_at":"2021-06-09T17:46:22Z","abstract_excerpt":"Attention-based neural networks have achieved state-of-the-art results on a wide range of tasks. Most such models use deterministic attention while stochastic attention is less explored due to the optimization difficulties or complicated model design. This paper introduces Bayesian attention belief networks, which construct a decoder network by modeling unnormalized attention weights with a hierarchy of gamma distributions, and an encoder network by stacking Weibull distributions with a deterministic-upward-stochastic-downward structure to approximate the posterior. The resulting auto-encoding"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.05251","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/2106.05251/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":"2026-07-05T02:47:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mTCD/RI6t4TWlHm9B1xn8pQbCj9/lpD7deR2L5TRzqvHiar9Wks6nfnMu3ftwI4RP9NENh6jCxh9fqUZOBrcCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T18:16:11.985017Z"},"content_sha256":"47bfc1d43f60dc31459fc500f136e8aaab582fc77f5e926b36aeff738ef5619c","schema_version":"1.0","event_id":"sha256:47bfc1d43f60dc31459fc500f136e8aaab582fc77f5e926b36aeff738ef5619c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SDOC44INWNHBS7VFZ6M4UEIVZD/bundle.json","state_url":"https://pith.science/pith/SDOC44INWNHBS7VFZ6M4UEIVZD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SDOC44INWNHBS7VFZ6M4UEIVZD/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":"2026-08-06T18:16:11Z","links":{"resolver":"https://pith.science/pith/SDOC44INWNHBS7VFZ6M4UEIVZD","bundle":"https://pith.science/pith/SDOC44INWNHBS7VFZ6M4UEIVZD/bundle.json","state":"https://pith.science/pith/SDOC44INWNHBS7VFZ6M4UEIVZD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SDOC44INWNHBS7VFZ6M4UEIVZD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:SDOC44INWNHBS7VFZ6M4UEIVZD","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":"e826ca01311760ea779017328207da1bdb2f6681ac4db6565014389df078aecd","cross_cats_sorted":["cs.CL","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-09T17:46:22Z","title_canon_sha256":"21ec2467c2e17064bdec8bacad6172babeda3de99d062d18ea7f3d93584a5283"},"schema_version":"1.0","source":{"id":"2106.05251","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.05251","created_at":"2026-07-05T02:47:54Z"},{"alias_kind":"arxiv_version","alias_value":"2106.05251v1","created_at":"2026-07-05T02:47:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.05251","created_at":"2026-07-05T02:47:54Z"},{"alias_kind":"pith_short_12","alias_value":"SDOC44INWNHB","created_at":"2026-07-05T02:47:54Z"},{"alias_kind":"pith_short_16","alias_value":"SDOC44INWNHBS7VF","created_at":"2026-07-05T02:47:54Z"},{"alias_kind":"pith_short_8","alias_value":"SDOC44IN","created_at":"2026-07-05T02:47:54Z"}],"graph_snapshots":[{"event_id":"sha256:47bfc1d43f60dc31459fc500f136e8aaab582fc77f5e926b36aeff738ef5619c","target":"graph","created_at":"2026-07-05T02:47:54Z","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/2106.05251/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Attention-based neural networks have achieved state-of-the-art results on a wide range of tasks. Most such models use deterministic attention while stochastic attention is less explored due to the optimization difficulties or complicated model design. This paper introduces Bayesian attention belief networks, which construct a decoder network by modeling unnormalized attention weights with a hierarchy of gamma distributions, and an encoder network by stacking Weibull distributions with a deterministic-upward-stochastic-downward structure to approximate the posterior. The resulting auto-encoding","authors_text":"Bo Chen, Mingyuan Zhou, Shujian Zhang, Xinjie Fan","cross_cats":["cs.CL","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-09T17:46:22Z","title":"Bayesian Attention Belief Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.05251","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":"sha256:2423ae255900dfe9b8f67c1231693774b198a46b24048a16855e28b9c77709c0","target":"record","created_at":"2026-07-05T02:47:54Z","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":"e826ca01311760ea779017328207da1bdb2f6681ac4db6565014389df078aecd","cross_cats_sorted":["cs.CL","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-09T17:46:22Z","title_canon_sha256":"21ec2467c2e17064bdec8bacad6172babeda3de99d062d18ea7f3d93584a5283"},"schema_version":"1.0","source":{"id":"2106.05251","kind":"arxiv","version":1}},"canonical_sha256":"90dc2e710db34e197ea5cf99ca1115c8fc2e5925474e31794f1e6406a4ded64b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"90dc2e710db34e197ea5cf99ca1115c8fc2e5925474e31794f1e6406a4ded64b","first_computed_at":"2026-07-05T02:47:54.786732Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:47:54.786732Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kYos2v4fKcAKDoM2uz00CzmEHBo3PeyftvczZS2OcdisFzcs4pkWyJskgW9T28e43IvaIUg6BLMFw9UB5Cw2CA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:47:54.787221Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.05251","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2423ae255900dfe9b8f67c1231693774b198a46b24048a16855e28b9c77709c0","sha256:47bfc1d43f60dc31459fc500f136e8aaab582fc77f5e926b36aeff738ef5619c"],"state_sha256":"3e800a569c45e22ab5458ca699e18626a3dc4234ed882dca0b82afb61ac37daa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FuHDF4gnpAFFeCxSaXtlApKRkaKulMWOwpQmf2WExzGv9c2wpPsh8bLU2JKRvyT0g3NLWSnf4+mDacYTRpxjDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T18:16:11.990696Z","bundle_sha256":"30ab9f82d1fc0eaa36bac7438c8bc68a5b4cbaebf5350cc6cb7d96b6afaa9b08"}}