{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:HLJMSDNX6CUR2PYUNZ5KEQ4OCV","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":"0651d008fcfd4ad4657b49cad8e985ae53e54ee3dbf4ef869fc4fd2c5648a4ef","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-05T05:14:22Z","title_canon_sha256":"4bfc4d80e030646fadfefb821aef97e49183f2e439b514c1ced93fca99c9130a"},"schema_version":"1.0","source":{"id":"2105.01868","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.01868","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"arxiv_version","alias_value":"2105.01868v1","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.01868","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"pith_short_12","alias_value":"HLJMSDNX6CUR","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"pith_short_16","alias_value":"HLJMSDNX6CUR2PYU","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"pith_short_8","alias_value":"HLJMSDNX","created_at":"2026-07-05T02:37:49Z"}],"graph_snapshots":[{"event_id":"sha256:e8cc4e467e80f976e2f0190614974e789cdc3f465a812bde868ed70ae7ba4ed6","target":"graph","created_at":"2026-07-05T02:37:49Z","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/2105.01868/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Various post-training uniform quantization methods have usually been studied based on convex optimization. As a result, most previous ones rely on the quantization error minimization and/or quadratic approximations. Such approaches are computationally efficient and reasonable when a large number of quantization bits are employed. When the number of quantization bits is relatively low, however, non-convex optimization is unavoidable to improve model accuracy. In this paper, we propose a new post-training uniform quantization technique considering non-convexity. We empirically show that hyper-pa","authors_text":"Baeseong Park, Byeongwook Kim, Daehwan Oh, Dongsoo Lee, Se Jung Kwon, Yeonju Ro, Yongkweon Jeon","cross_cats":["math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-05T05:14:22Z","title":"Q-Rater: Non-Convex Optimization for Post-Training Uniform Quantization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.01868","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:f789d58b4d6528f46a38f9e040edcd3549430e951ac5849e436960fc4d195b21","target":"record","created_at":"2026-07-05T02:37:49Z","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":"0651d008fcfd4ad4657b49cad8e985ae53e54ee3dbf4ef869fc4fd2c5648a4ef","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-05T05:14:22Z","title_canon_sha256":"4bfc4d80e030646fadfefb821aef97e49183f2e439b514c1ced93fca99c9130a"},"schema_version":"1.0","source":{"id":"2105.01868","kind":"arxiv","version":1}},"canonical_sha256":"3ad2c90db7f0a91d3f146e7aa2438e156b2c25f71a7212b987a6839ff902bd28","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3ad2c90db7f0a91d3f146e7aa2438e156b2c25f71a7212b987a6839ff902bd28","first_computed_at":"2026-07-05T02:37:49.556135Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:37:49.556135Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1Hz1pDR8VKcYMtVQEu53NOGTjTlEaTyeF+vig8BFmKpNy0O7m247nGtRNwrfRfO/H/Ienx17jmepEwhRjZipBw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:37:49.556751Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.01868","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f789d58b4d6528f46a38f9e040edcd3549430e951ac5849e436960fc4d195b21","sha256:e8cc4e467e80f976e2f0190614974e789cdc3f465a812bde868ed70ae7ba4ed6"],"state_sha256":"ba05ffe35e42d32cab35d773d0d6a8fb05e4e81f9008e48f405408af3b48382d"}