{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CPIDXYIARRIJQQW4CGJDL6L36F","short_pith_number":"pith:CPIDXYIA","schema_version":"1.0","canonical_sha256":"13d03be1008c509842dc119235f97bf17a313b565b55445749d89844c4929add","source":{"kind":"arxiv","id":"2110.03553","version":1},"attestation_state":"computed","paper":{"title":"Shift-BNN: Highly-Efficient Probabilistic Bayesian Neural Network Training via Memory-Friendly Pattern Retrieving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AR","authors_text":"Haojun Xia, Lening Wang, Qiyu Wan, Shuaiwen Leon Song, Xin Fu, Xingyao Zhang","submitted_at":"2021-10-07T15:20:53Z","abstract_excerpt":"Bayesian Neural Networks (BNNs) that possess a property of uncertainty estimation have been increasingly adopted in a wide range of safety-critical AI applications which demand reliable and robust decision making, e.g., self-driving, rescue robots, medical image diagnosis. The training procedure of a probabilistic BNN model involves training an ensemble of sampled DNN models, which induces orders of magnitude larger volume of data movement than training a single DNN model. In this paper, we reveal that the root cause for BNN training inefficiency originates from the massive off-chip data trans"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2110.03553","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2021-10-07T15:20:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"46217153f1ed2d71e3d3a060d744f90164858e4915d4ddc013e09b443d2116f2","abstract_canon_sha256":"8158a0df62182b240448fcac941572cc78c2b6db74ea886b5d6d3b45ff887030"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:20:43.809878Z","signature_b64":"wlDWUBZwqWkVcG/fPMsOWKqRO0edgf7CYL14Sj2Zhm4PQoM3jRV/3Wx0qZZG0IsMfZcdyulh8PFrHmTJSghdAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13d03be1008c509842dc119235f97bf17a313b565b55445749d89844c4929add","last_reissued_at":"2026-07-05T03:20:43.809466Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:20:43.809466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Shift-BNN: Highly-Efficient Probabilistic Bayesian Neural Network Training via Memory-Friendly Pattern Retrieving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AR","authors_text":"Haojun Xia, Lening Wang, Qiyu Wan, Shuaiwen Leon Song, Xin Fu, Xingyao Zhang","submitted_at":"2021-10-07T15:20:53Z","abstract_excerpt":"Bayesian Neural Networks (BNNs) that possess a property of uncertainty estimation have been increasingly adopted in a wide range of safety-critical AI applications which demand reliable and robust decision making, e.g., self-driving, rescue robots, medical image diagnosis. The training procedure of a probabilistic BNN model involves training an ensemble of sampled DNN models, which induces orders of magnitude larger volume of data movement than training a single DNN model. In this paper, we reveal that the root cause for BNN training inefficiency originates from the massive off-chip data trans"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.03553","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/2110.03553/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2110.03553","created_at":"2026-07-05T03:20:43.809522+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.03553v1","created_at":"2026-07-05T03:20:43.809522+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.03553","created_at":"2026-07-05T03:20:43.809522+00:00"},{"alias_kind":"pith_short_12","alias_value":"CPIDXYIARRIJ","created_at":"2026-07-05T03:20:43.809522+00:00"},{"alias_kind":"pith_short_16","alias_value":"CPIDXYIARRIJQQW4","created_at":"2026-07-05T03:20:43.809522+00:00"},{"alias_kind":"pith_short_8","alias_value":"CPIDXYIA","created_at":"2026-07-05T03:20:43.809522+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CPIDXYIARRIJQQW4CGJDL6L36F","json":"https://pith.science/pith/CPIDXYIARRIJQQW4CGJDL6L36F.json","graph_json":"https://pith.science/api/pith-number/CPIDXYIARRIJQQW4CGJDL6L36F/graph.json","events_json":"https://pith.science/api/pith-number/CPIDXYIARRIJQQW4CGJDL6L36F/events.json","paper":"https://pith.science/paper/CPIDXYIA"},"agent_actions":{"view_html":"https://pith.science/pith/CPIDXYIARRIJQQW4CGJDL6L36F","download_json":"https://pith.science/pith/CPIDXYIARRIJQQW4CGJDL6L36F.json","view_paper":"https://pith.science/paper/CPIDXYIA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.03553&json=true","fetch_graph":"https://pith.science/api/pith-number/CPIDXYIARRIJQQW4CGJDL6L36F/graph.json","fetch_events":"https://pith.science/api/pith-number/CPIDXYIARRIJQQW4CGJDL6L36F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CPIDXYIARRIJQQW4CGJDL6L36F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CPIDXYIARRIJQQW4CGJDL6L36F/action/storage_attestation","attest_author":"https://pith.science/pith/CPIDXYIARRIJQQW4CGJDL6L36F/action/author_attestation","sign_citation":"https://pith.science/pith/CPIDXYIARRIJQQW4CGJDL6L36F/action/citation_signature","submit_replication":"https://pith.science/pith/CPIDXYIARRIJQQW4CGJDL6L36F/action/replication_record"}},"created_at":"2026-07-05T03:20:43.809522+00:00","updated_at":"2026-07-05T03:20:43.809522+00:00"}