{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:PZQYXXIYE5WLD7JTHO3EWNTQF6","short_pith_number":"pith:PZQYXXIY","canonical_record":{"source":{"id":"2305.14765","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-05-24T06:16:11Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8bd4abc271e486106002c1e40082ae80438cd2ac059909b1acf207d8b70b901d","abstract_canon_sha256":"816aaede0caf5a0ddb2b6e1d092e4b75b3de2afb6d0b93f49b8040d534060079"},"schema_version":"1.0"},"canonical_sha256":"7e618bdd18276cb1fd333bb64b36702f95347fc07052426e10ec26be84fb0d56","source":{"kind":"arxiv","id":"2305.14765","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.14765","created_at":"2026-07-05T06:13:27Z"},{"alias_kind":"arxiv_version","alias_value":"2305.14765v1","created_at":"2026-07-05T06:13:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.14765","created_at":"2026-07-05T06:13:27Z"},{"alias_kind":"pith_short_12","alias_value":"PZQYXXIYE5WL","created_at":"2026-07-05T06:13:27Z"},{"alias_kind":"pith_short_16","alias_value":"PZQYXXIYE5WLD7JT","created_at":"2026-07-05T06:13:27Z"},{"alias_kind":"pith_short_8","alias_value":"PZQYXXIY","created_at":"2026-07-05T06:13:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:PZQYXXIYE5WLD7JTHO3EWNTQF6","target":"record","payload":{"canonical_record":{"source":{"id":"2305.14765","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-05-24T06:16:11Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8bd4abc271e486106002c1e40082ae80438cd2ac059909b1acf207d8b70b901d","abstract_canon_sha256":"816aaede0caf5a0ddb2b6e1d092e4b75b3de2afb6d0b93f49b8040d534060079"},"schema_version":"1.0"},"canonical_sha256":"7e618bdd18276cb1fd333bb64b36702f95347fc07052426e10ec26be84fb0d56","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:13:27.727620Z","signature_b64":"ljvt7wpv1IZgafqJjMp9AXIniiKxZ4Jwv8Uwm0CmzXoOnn8JNVFkI2vXY5iVzp2d7UL4qVV4zysQyOxG7BWDAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e618bdd18276cb1fd333bb64b36702f95347fc07052426e10ec26be84fb0d56","last_reissued_at":"2026-07-05T06:13:27.727148Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:13:27.727148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.14765","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":"2026-07-05T06:13:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/w8X7qdP5dlLaSRgcecDfExpKkG+0RhS4EuoWLaN5Fq5aKhLnrU8uSoR3unt8QYraScv4lqG/nY6TuiiGPH4BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T14:10:27.334981Z"},"content_sha256":"e0443f7e9c72c6da51316e19e5bff785740583f61f4a815b574a2910a73fe9a7","schema_version":"1.0","event_id":"sha256:e0443f7e9c72c6da51316e19e5bff785740583f61f4a815b574a2910a73fe9a7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:PZQYXXIYE5WLD7JTHO3EWNTQF6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Dongyoon Yang, Gyuseung Baek, Ilsang Ohn, Insung Kong, Jongjin Lee, Yongdai Kim","submitted_at":"2023-05-24T06:16:11Z","abstract_excerpt":"Bayesian approaches for learning deep neural networks (BNN) have been received much attention and successfully applied to various applications. Particularly, BNNs have the merit of having better generalization ability as well as better uncertainty quantification. For the success of BNN, search an appropriate architecture of the neural networks is an important task, and various algorithms to find good sparse neural networks have been proposed. In this paper, we propose a new node-sparse BNN model which has good theoretical properties and is computationally feasible. We prove that the posterior "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.14765","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/2305.14765/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":"2026-07-05T06:13:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xgSQyq6yLr/LD1+joWhHS2fHnRPU+cAUJGdK/JbLUof/Wm8pJrJHvSJDzmiqnvI5kzU/uKOOZJ59uJNH0tsWCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T14:10:27.335973Z"},"content_sha256":"fc33291876e7d1127ae737c92d9cc5cf72064fbb24cf562ed42f0cf289ebf2c4","schema_version":"1.0","event_id":"sha256:fc33291876e7d1127ae737c92d9cc5cf72064fbb24cf562ed42f0cf289ebf2c4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PZQYXXIYE5WLD7JTHO3EWNTQF6/bundle.json","state_url":"https://pith.science/pith/PZQYXXIYE5WLD7JTHO3EWNTQF6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PZQYXXIYE5WLD7JTHO3EWNTQF6/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":"2026-08-21T14:10:27Z","links":{"resolver":"https://pith.science/pith/PZQYXXIYE5WLD7JTHO3EWNTQF6","bundle":"https://pith.science/pith/PZQYXXIYE5WLD7JTHO3EWNTQF6/bundle.json","state":"https://pith.science/pith/PZQYXXIYE5WLD7JTHO3EWNTQF6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PZQYXXIYE5WLD7JTHO3EWNTQF6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:PZQYXXIYE5WLD7JTHO3EWNTQF6","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":"816aaede0caf5a0ddb2b6e1d092e4b75b3de2afb6d0b93f49b8040d534060079","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-05-24T06:16:11Z","title_canon_sha256":"8bd4abc271e486106002c1e40082ae80438cd2ac059909b1acf207d8b70b901d"},"schema_version":"1.0","source":{"id":"2305.14765","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.14765","created_at":"2026-07-05T06:13:27Z"},{"alias_kind":"arxiv_version","alias_value":"2305.14765v1","created_at":"2026-07-05T06:13:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.14765","created_at":"2026-07-05T06:13:27Z"},{"alias_kind":"pith_short_12","alias_value":"PZQYXXIYE5WL","created_at":"2026-07-05T06:13:27Z"},{"alias_kind":"pith_short_16","alias_value":"PZQYXXIYE5WLD7JT","created_at":"2026-07-05T06:13:27Z"},{"alias_kind":"pith_short_8","alias_value":"PZQYXXIY","created_at":"2026-07-05T06:13:27Z"}],"graph_snapshots":[{"event_id":"sha256:fc33291876e7d1127ae737c92d9cc5cf72064fbb24cf562ed42f0cf289ebf2c4","target":"graph","created_at":"2026-07-05T06:13:27Z","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/2305.14765/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian approaches for learning deep neural networks (BNN) have been received much attention and successfully applied to various applications. Particularly, BNNs have the merit of having better generalization ability as well as better uncertainty quantification. For the success of BNN, search an appropriate architecture of the neural networks is an important task, and various algorithms to find good sparse neural networks have been proposed. In this paper, we propose a new node-sparse BNN model which has good theoretical properties and is computationally feasible. We prove that the posterior ","authors_text":"Dongyoon Yang, Gyuseung Baek, Ilsang Ohn, Insung Kong, Jongjin Lee, Yongdai Kim","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-05-24T06:16:11Z","title":"Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.14765","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:e0443f7e9c72c6da51316e19e5bff785740583f61f4a815b574a2910a73fe9a7","target":"record","created_at":"2026-07-05T06:13:27Z","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":"816aaede0caf5a0ddb2b6e1d092e4b75b3de2afb6d0b93f49b8040d534060079","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-05-24T06:16:11Z","title_canon_sha256":"8bd4abc271e486106002c1e40082ae80438cd2ac059909b1acf207d8b70b901d"},"schema_version":"1.0","source":{"id":"2305.14765","kind":"arxiv","version":1}},"canonical_sha256":"7e618bdd18276cb1fd333bb64b36702f95347fc07052426e10ec26be84fb0d56","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7e618bdd18276cb1fd333bb64b36702f95347fc07052426e10ec26be84fb0d56","first_computed_at":"2026-07-05T06:13:27.727148Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:13:27.727148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ljvt7wpv1IZgafqJjMp9AXIniiKxZ4Jwv8Uwm0CmzXoOnn8JNVFkI2vXY5iVzp2d7UL4qVV4zysQyOxG7BWDAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:13:27.727620Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.14765","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e0443f7e9c72c6da51316e19e5bff785740583f61f4a815b574a2910a73fe9a7","sha256:fc33291876e7d1127ae737c92d9cc5cf72064fbb24cf562ed42f0cf289ebf2c4"],"state_sha256":"343e97f9834ec98373c9bcfa5e99630c3833736b1bc328e6c71fbd727e61adc7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hxa1GuEd63DaC0OsbeDe5SmN9lzycrIgZw/+CaOb/Ja1xHAs+6RxQ7f7vAswD/E1XfAVxOfyC9bsv9SDxYnrBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T14:10:27.343493Z","bundle_sha256":"ca6aa1394f88af95cc4e1abc109f197437199c63a149657822af782106071300"}}