{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:WLWAE3VXI2GNTFJNPYFSWR7GRM","short_pith_number":"pith:WLWAE3VX","schema_version":"1.0","canonical_sha256":"b2ec026eb7468cd9952d7e0b2b47e68b21ba5def6bac73d8229e3c73c350890d","source":{"kind":"arxiv","id":"1711.11294","version":1},"attestation_state":"computed","paper":{"title":"Towards Accurate Binary Convolutional Neural Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Cong Zhao, Wei Pan, Xiaofan Lin","submitted_at":"2017-11-30T09:58:14Z","abstract_excerpt":"We introduce a novel scheme to train binary convolutional neural networks (CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time. It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bitwise operations, leading to much faster test-time inference and lower power consumption. However, previous works on binarizing CNNs usually result in severe prediction accuracy degradation. In this paper, we address this issue with two major innovations: (1) approximating full-pr"},"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":"1711.11294","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2017-11-30T09:58:14Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b3db5e93e4481395080bdad618fd7c3ed8d561ce64a57c9b3cbffa8a1a05b569","abstract_canon_sha256":"f01c5b401a060342cb5373fb38eb117e5b8446b03f9aea6c95c15dce0f397c69"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:11.918571Z","signature_b64":"uS3gTE8W/AsD3BmLb3PxRwUSHJZayhhmOUdto+Ka8/VvqEr+RHzDoXL/xA1r3WIyKiAsguDk3jN6KyK7OEW1Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2ec026eb7468cd9952d7e0b2b47e68b21ba5def6bac73d8229e3c73c350890d","last_reissued_at":"2026-05-18T00:29:11.917941Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:11.917941Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Accurate Binary Convolutional Neural Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Cong Zhao, Wei Pan, Xiaofan Lin","submitted_at":"2017-11-30T09:58:14Z","abstract_excerpt":"We introduce a novel scheme to train binary convolutional neural networks (CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time. It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bitwise operations, leading to much faster test-time inference and lower power consumption. However, previous works on binarizing CNNs usually result in severe prediction accuracy degradation. In this paper, we address this issue with two major innovations: (1) approximating full-pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.11294","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":""},"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":"1711.11294","created_at":"2026-05-18T00:29:11.918031+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.11294v1","created_at":"2026-05-18T00:29:11.918031+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.11294","created_at":"2026-05-18T00:29:11.918031+00:00"},{"alias_kind":"pith_short_12","alias_value":"WLWAE3VXI2GN","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_16","alias_value":"WLWAE3VXI2GNTFJN","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_8","alias_value":"WLWAE3VX","created_at":"2026-05-18T12:31:53.515858+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/WLWAE3VXI2GNTFJNPYFSWR7GRM","json":"https://pith.science/pith/WLWAE3VXI2GNTFJNPYFSWR7GRM.json","graph_json":"https://pith.science/api/pith-number/WLWAE3VXI2GNTFJNPYFSWR7GRM/graph.json","events_json":"https://pith.science/api/pith-number/WLWAE3VXI2GNTFJNPYFSWR7GRM/events.json","paper":"https://pith.science/paper/WLWAE3VX"},"agent_actions":{"view_html":"https://pith.science/pith/WLWAE3VXI2GNTFJNPYFSWR7GRM","download_json":"https://pith.science/pith/WLWAE3VXI2GNTFJNPYFSWR7GRM.json","view_paper":"https://pith.science/paper/WLWAE3VX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.11294&json=true","fetch_graph":"https://pith.science/api/pith-number/WLWAE3VXI2GNTFJNPYFSWR7GRM/graph.json","fetch_events":"https://pith.science/api/pith-number/WLWAE3VXI2GNTFJNPYFSWR7GRM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WLWAE3VXI2GNTFJNPYFSWR7GRM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WLWAE3VXI2GNTFJNPYFSWR7GRM/action/storage_attestation","attest_author":"https://pith.science/pith/WLWAE3VXI2GNTFJNPYFSWR7GRM/action/author_attestation","sign_citation":"https://pith.science/pith/WLWAE3VXI2GNTFJNPYFSWR7GRM/action/citation_signature","submit_replication":"https://pith.science/pith/WLWAE3VXI2GNTFJNPYFSWR7GRM/action/replication_record"}},"created_at":"2026-05-18T00:29:11.918031+00:00","updated_at":"2026-05-18T00:29:11.918031+00:00"}