{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:ACF43IDOJEKTWY7Z32J3MYV667","short_pith_number":"pith:ACF43IDO","canonical_record":{"source":{"id":"1905.00789","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-02T14:53:24Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"0e712f88c07ba4cfd988665b7de829cbc3bece7ea2a96cdc8313d7a74fed04c7","abstract_canon_sha256":"e9076aba9feaa2eb664855bbed26c8787ab7dfec9226f712cba497ea7dd2a852"},"schema_version":"1.0"},"canonical_sha256":"008bcda06e49153b63f9de93b662bef7f2919dab368ea4e7e572e8773857998f","source":{"kind":"arxiv","id":"1905.00789","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.00789","created_at":"2026-05-17T23:47:09Z"},{"alias_kind":"arxiv_version","alias_value":"1905.00789v1","created_at":"2026-05-17T23:47:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.00789","created_at":"2026-05-17T23:47:09Z"},{"alias_kind":"pith_short_12","alias_value":"ACF43IDOJEKT","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"ACF43IDOJEKTWY7Z","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"ACF43IDO","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:ACF43IDOJEKTWY7Z32J3MYV667","target":"record","payload":{"canonical_record":{"source":{"id":"1905.00789","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-02T14:53:24Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"0e712f88c07ba4cfd988665b7de829cbc3bece7ea2a96cdc8313d7a74fed04c7","abstract_canon_sha256":"e9076aba9feaa2eb664855bbed26c8787ab7dfec9226f712cba497ea7dd2a852"},"schema_version":"1.0"},"canonical_sha256":"008bcda06e49153b63f9de93b662bef7f2919dab368ea4e7e572e8773857998f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:09.302529Z","signature_b64":"c5DLcylbSKxn2tOx96t90ZxKOLG65W0qhZjVNpauqSoK6UhOma6Tk7AGSCZhcnD9KuqWwWRdH/hyMXzxcfgeDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"008bcda06e49153b63f9de93b662bef7f2919dab368ea4e7e572e8773857998f","last_reissued_at":"2026-05-17T23:47:09.301886Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:09.301886Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.00789","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":"2026-05-17T23:47:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oZlUOWkNHChDddc1Iw/3TcxRL8HtVd1Lzp2kEK8nTSGKprXYp1peLcyDrOjZ4S7dqlOp4S29hph38UvJBU0RCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T16:15:43.188753Z"},"content_sha256":"c1c4021aa64154f061f92c010ac2a92498eb4b9fd59aa71256f1950fba6860f4","schema_version":"1.0","event_id":"sha256:c1c4021aa64154f061f92c010ac2a92498eb4b9fd59aa71256f1950fba6860f4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:ACF43IDOJEKTWY7Z32J3MYV667","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Geng Yuan, Kaisheng Ma, Shaokai Ye, Sheng Lin, Xiaolong Ma, Yanzhi Wang","submitted_at":"2019-05-02T14:53:24Z","abstract_excerpt":"Weight quantization is one of the most important techniques of Deep Neural Networks (DNNs) model compression method. A recent work using systematic framework of DNN weight quantization with the advanced optimization algorithm ADMM (Alternating Direction Methods of Multipliers) achieves one of state-of-art results in weight quantization. In this work, we first extend such ADMM-based framework to guarantee solution feasibility and we have further developed a multi-step, progressive DNN weight quantization framework, with dual benefits of (i) achieving further weight quantization thanks to the sp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.00789","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:47:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lFED6jD9mdr6VbslkQMIdiRbBbPs7oXF/CqHYmiVH4iQpyswsChJnNZpbjrKRcLnnPL5IOkxePgQOCSLUJiLCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T16:15:43.189118Z"},"content_sha256":"9f0fe9d469c4c41cffcb2fcc83d63235978b15e5c8f7cbb73c069e56bdf4e7ae","schema_version":"1.0","event_id":"sha256:9f0fe9d469c4c41cffcb2fcc83d63235978b15e5c8f7cbb73c069e56bdf4e7ae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ACF43IDOJEKTWY7Z32J3MYV667/bundle.json","state_url":"https://pith.science/pith/ACF43IDOJEKTWY7Z32J3MYV667/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ACF43IDOJEKTWY7Z32J3MYV667/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":"2026-05-21T16:15:43Z","links":{"resolver":"https://pith.science/pith/ACF43IDOJEKTWY7Z32J3MYV667","bundle":"https://pith.science/pith/ACF43IDOJEKTWY7Z32J3MYV667/bundle.json","state":"https://pith.science/pith/ACF43IDOJEKTWY7Z32J3MYV667/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ACF43IDOJEKTWY7Z32J3MYV667/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:ACF43IDOJEKTWY7Z32J3MYV667","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":"e9076aba9feaa2eb664855bbed26c8787ab7dfec9226f712cba497ea7dd2a852","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-02T14:53:24Z","title_canon_sha256":"0e712f88c07ba4cfd988665b7de829cbc3bece7ea2a96cdc8313d7a74fed04c7"},"schema_version":"1.0","source":{"id":"1905.00789","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.00789","created_at":"2026-05-17T23:47:09Z"},{"alias_kind":"arxiv_version","alias_value":"1905.00789v1","created_at":"2026-05-17T23:47:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.00789","created_at":"2026-05-17T23:47:09Z"},{"alias_kind":"pith_short_12","alias_value":"ACF43IDOJEKT","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"ACF43IDOJEKTWY7Z","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"ACF43IDO","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:9f0fe9d469c4c41cffcb2fcc83d63235978b15e5c8f7cbb73c069e56bdf4e7ae","target":"graph","created_at":"2026-05-17T23:47:09Z","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"},"paper":{"abstract_excerpt":"Weight quantization is one of the most important techniques of Deep Neural Networks (DNNs) model compression method. A recent work using systematic framework of DNN weight quantization with the advanced optimization algorithm ADMM (Alternating Direction Methods of Multipliers) achieves one of state-of-art results in weight quantization. In this work, we first extend such ADMM-based framework to guarantee solution feasibility and we have further developed a multi-step, progressive DNN weight quantization framework, with dual benefits of (i) achieving further weight quantization thanks to the sp","authors_text":"Geng Yuan, Kaisheng Ma, Shaokai Ye, Sheng Lin, Xiaolong Ma, Yanzhi Wang","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-02T14:53:24Z","title":"Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.00789","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:c1c4021aa64154f061f92c010ac2a92498eb4b9fd59aa71256f1950fba6860f4","target":"record","created_at":"2026-05-17T23:47:09Z","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":"e9076aba9feaa2eb664855bbed26c8787ab7dfec9226f712cba497ea7dd2a852","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-02T14:53:24Z","title_canon_sha256":"0e712f88c07ba4cfd988665b7de829cbc3bece7ea2a96cdc8313d7a74fed04c7"},"schema_version":"1.0","source":{"id":"1905.00789","kind":"arxiv","version":1}},"canonical_sha256":"008bcda06e49153b63f9de93b662bef7f2919dab368ea4e7e572e8773857998f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"008bcda06e49153b63f9de93b662bef7f2919dab368ea4e7e572e8773857998f","first_computed_at":"2026-05-17T23:47:09.301886Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:47:09.301886Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"c5DLcylbSKxn2tOx96t90ZxKOLG65W0qhZjVNpauqSoK6UhOma6Tk7AGSCZhcnD9KuqWwWRdH/hyMXzxcfgeDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:47:09.302529Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.00789","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c1c4021aa64154f061f92c010ac2a92498eb4b9fd59aa71256f1950fba6860f4","sha256:9f0fe9d469c4c41cffcb2fcc83d63235978b15e5c8f7cbb73c069e56bdf4e7ae"],"state_sha256":"1bc7a0d88fd227554e9f7b04c152fc34f86d6b696118fba6435d9c1b923fe76c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+GFHpMWJnHpBffHDPHPwnhzUq3NUOw2B4E8iyNa+yS7tVjqhzvmRyrX6hFhAWK1XPIm8HIDkGTKmpuTSf39oBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T16:15:43.191598Z","bundle_sha256":"f691b1f0f5e3c1824ac63a9924f454b9f5a885f0510c4e9f14ac626ea3339149"}}