{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:7RTLYFQ7AIN6I5W2SNV2CSYHJZ","short_pith_number":"pith:7RTLYFQ7","canonical_record":{"source":{"id":"2112.00133","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-30T22:05:59Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"56d38b2ea0f724a0a7549225f5d1f390dbf29356e46c1df7fc0f23242b79e786","abstract_canon_sha256":"59bd6f5917fc456aa8b77e2854bc61dd3b238ce962db5d48c699fb4d0f483033"},"schema_version":"1.0"},"canonical_sha256":"fc66bc161f021be476da936ba14b074e59c7550565fc8cc99b99697fa53b9a5f","source":{"kind":"arxiv","id":"2112.00133","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.00133","created_at":"2026-07-05T04:18:49Z"},{"alias_kind":"arxiv_version","alias_value":"2112.00133v2","created_at":"2026-07-05T04:18:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.00133","created_at":"2026-07-05T04:18:49Z"},{"alias_kind":"pith_short_12","alias_value":"7RTLYFQ7AIN6","created_at":"2026-07-05T04:18:49Z"},{"alias_kind":"pith_short_16","alias_value":"7RTLYFQ7AIN6I5W2","created_at":"2026-07-05T04:18:49Z"},{"alias_kind":"pith_short_8","alias_value":"7RTLYFQ7","created_at":"2026-07-05T04:18:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:7RTLYFQ7AIN6I5W2SNV2CSYHJZ","target":"record","payload":{"canonical_record":{"source":{"id":"2112.00133","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-30T22:05:59Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"56d38b2ea0f724a0a7549225f5d1f390dbf29356e46c1df7fc0f23242b79e786","abstract_canon_sha256":"59bd6f5917fc456aa8b77e2854bc61dd3b238ce962db5d48c699fb4d0f483033"},"schema_version":"1.0"},"canonical_sha256":"fc66bc161f021be476da936ba14b074e59c7550565fc8cc99b99697fa53b9a5f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:18:49.675016Z","signature_b64":"JBrIzVSUqmQe+JsuhIqQ2grPxnAxva8kk5J6kHVfRmS777TKNzOPEcYDOBP687BTvnjtQRFIGowx9sVzRBG1Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc66bc161f021be476da936ba14b074e59c7550565fc8cc99b99697fa53b9a5f","last_reissued_at":"2026-07-05T04:18:49.674608Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:18:49.674608Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2112.00133","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:18:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lSK4D4ZaiV7QuIvlIdyr8tiMoMj1rH0o4sfgSq2DW+yZ6kBMtYYGsdJHSVMeq9fjF/hgbpK/DnXbvSiJB2JvAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:22:10.783738Z"},"content_sha256":"ed20fbed2b0c5cd62d7e97ec1af5c8891cfa72c2fd850a35613fff7a46c653f0","schema_version":"1.0","event_id":"sha256:ed20fbed2b0c5cd62d7e97ec1af5c8891cfa72c2fd850a35613fff7a46c653f0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:7RTLYFQ7AIN6I5W2SNV2CSYHJZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PokeBNN: A Binary Pursuit of Lightweight Accuracy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Lukasz Lew, Yichi Zhang, Zhiru Zhang","submitted_at":"2021-11-30T22:05:59Z","abstract_excerpt":"Optimization of Top-1 ImageNet promotes enormous networks that may be impractical in inference settings. Binary neural networks (BNNs) have the potential to significantly lower the compute intensity but existing models suffer from low quality. To overcome this deficiency, we propose PokeConv, a binary convolution block which improves quality of BNNs by techniques such as adding multiple residual paths, and tuning the activation function. We apply it to ResNet-50 and optimize ResNet's initial convolutional layer which is hard to binarize. We name the resulting network family PokeBNN. These tech"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.00133","kind":"arxiv","version":2},"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/2112.00133/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":"2026-07-05T04:18:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7q4EpfMkOyvfNsePVjdimLy88iz+vXp27y2mTfhFfQY7HJRIFVnu6WcRKvJLw8uifkypSxhRaTlJqHZxbaHJDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:22:10.784111Z"},"content_sha256":"30b41120d0528c5d4feda72cd2ebd108ceea2f8fbb67b0aa38b3afa495f0b97f","schema_version":"1.0","event_id":"sha256:30b41120d0528c5d4feda72cd2ebd108ceea2f8fbb67b0aa38b3afa495f0b97f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7RTLYFQ7AIN6I5W2SNV2CSYHJZ/bundle.json","state_url":"https://pith.science/pith/7RTLYFQ7AIN6I5W2SNV2CSYHJZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7RTLYFQ7AIN6I5W2SNV2CSYHJZ/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":"2026-07-06T19:22:10Z","links":{"resolver":"https://pith.science/pith/7RTLYFQ7AIN6I5W2SNV2CSYHJZ","bundle":"https://pith.science/pith/7RTLYFQ7AIN6I5W2SNV2CSYHJZ/bundle.json","state":"https://pith.science/pith/7RTLYFQ7AIN6I5W2SNV2CSYHJZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7RTLYFQ7AIN6I5W2SNV2CSYHJZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:7RTLYFQ7AIN6I5W2SNV2CSYHJZ","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":"59bd6f5917fc456aa8b77e2854bc61dd3b238ce962db5d48c699fb4d0f483033","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-30T22:05:59Z","title_canon_sha256":"56d38b2ea0f724a0a7549225f5d1f390dbf29356e46c1df7fc0f23242b79e786"},"schema_version":"1.0","source":{"id":"2112.00133","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.00133","created_at":"2026-07-05T04:18:49Z"},{"alias_kind":"arxiv_version","alias_value":"2112.00133v2","created_at":"2026-07-05T04:18:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.00133","created_at":"2026-07-05T04:18:49Z"},{"alias_kind":"pith_short_12","alias_value":"7RTLYFQ7AIN6","created_at":"2026-07-05T04:18:49Z"},{"alias_kind":"pith_short_16","alias_value":"7RTLYFQ7AIN6I5W2","created_at":"2026-07-05T04:18:49Z"},{"alias_kind":"pith_short_8","alias_value":"7RTLYFQ7","created_at":"2026-07-05T04:18:49Z"}],"graph_snapshots":[{"event_id":"sha256:30b41120d0528c5d4feda72cd2ebd108ceea2f8fbb67b0aa38b3afa495f0b97f","target":"graph","created_at":"2026-07-05T04:18:49Z","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/2112.00133/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Optimization of Top-1 ImageNet promotes enormous networks that may be impractical in inference settings. Binary neural networks (BNNs) have the potential to significantly lower the compute intensity but existing models suffer from low quality. To overcome this deficiency, we propose PokeConv, a binary convolution block which improves quality of BNNs by techniques such as adding multiple residual paths, and tuning the activation function. We apply it to ResNet-50 and optimize ResNet's initial convolutional layer which is hard to binarize. We name the resulting network family PokeBNN. These tech","authors_text":"Lukasz Lew, Yichi Zhang, Zhiru Zhang","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-30T22:05:59Z","title":"PokeBNN: A Binary Pursuit of Lightweight Accuracy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.00133","kind":"arxiv","version":2},"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:ed20fbed2b0c5cd62d7e97ec1af5c8891cfa72c2fd850a35613fff7a46c653f0","target":"record","created_at":"2026-07-05T04:18:49Z","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":"59bd6f5917fc456aa8b77e2854bc61dd3b238ce962db5d48c699fb4d0f483033","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-30T22:05:59Z","title_canon_sha256":"56d38b2ea0f724a0a7549225f5d1f390dbf29356e46c1df7fc0f23242b79e786"},"schema_version":"1.0","source":{"id":"2112.00133","kind":"arxiv","version":2}},"canonical_sha256":"fc66bc161f021be476da936ba14b074e59c7550565fc8cc99b99697fa53b9a5f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fc66bc161f021be476da936ba14b074e59c7550565fc8cc99b99697fa53b9a5f","first_computed_at":"2026-07-05T04:18:49.674608Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:18:49.674608Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JBrIzVSUqmQe+JsuhIqQ2grPxnAxva8kk5J6kHVfRmS777TKNzOPEcYDOBP687BTvnjtQRFIGowx9sVzRBG1Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:18:49.675016Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.00133","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ed20fbed2b0c5cd62d7e97ec1af5c8891cfa72c2fd850a35613fff7a46c653f0","sha256:30b41120d0528c5d4feda72cd2ebd108ceea2f8fbb67b0aa38b3afa495f0b97f"],"state_sha256":"7045f8654a312ed56d52e53f09632e6c773e533ec4cebed62805e5c1720f079b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dSJWAeIeIWmJqcuugCd3gLGmmOfI8QuIJvNmDshB//3GQJXkS2acW/lwoeYxN8Wv4iQksSofDYV90qjp1q6LAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T19:22:10.786225Z","bundle_sha256":"af9d8e9fbaff5331a3db367370f18ae7ef8e2730e4cbf112b54673c5d62c2539"}}