{"id":"W4386596956","doi":"10.1109/icip49359.2023.10222555","title":"LKBQ: Pushing the Limit of Post-Training Quantization to Extreme 1 bit","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Quantization (signal processing); Initialization; Computer science; Artificial neural network; Binary number; Dither; Algorithm; Artificial intelligence; Speech recognition; Arithmetic; Mathematics; Computer network; Bandwidth (computing)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001597764,0.001024555,0.000800938,0.000404067,0.0006144377,0.001324963,0.00218688,0.001213017,0.004903125],"category_scores_gemma":[0.007014379,0.0004348325,0.0003441566,0.000556581,0.001307367,0.003687019,0.002688955,0.003309512,0.001346768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009252403,"about_ca_system_score_gemma":0.001186523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003335739,"about_ca_topic_score_gemma":0.005040144,"domain_scores_codex":[0.9988976,0.0002396203,0.00007335447,0.0002134524,0.0004595949,0.0001163016],"domain_scores_gemma":[0.997918,0.0008451096,0.00016004,0.0005608576,0.0004157838,0.0001003306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001152773,0.0002935942,0.002321069,0.0004644702,0.0001075846,0.0002242502,0.0003798707,0.1202126,0.07033496,0.05016364,0.01307855,0.7412667],"study_design_scores_gemma":[0.00007535089,0.0002317538,0.0005398359,0.00006581731,0.00002683462,0.000157341,0.00005389211,0.9131366,0.04450468,0.03468712,0.006475351,0.00004528595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03401678,0.00138865,0.9544145,0.0008326599,0.0002387328,0.0000968472,0.0001924949,0.004911864,0.003907425],"genre_scores_gemma":[0.5853193,0.0005658888,0.4056508,0.001098878,0.0001064692,0.0001998553,0.0005214135,0.0006821253,0.005855241],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004903125,"threshold_uncertainty_score":0.0164026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1124540804160531,"score_gpt":0.3023301855015795,"score_spread":0.1898761050855264,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}