{"id":"W4385605073","doi":"10.1016/j.neunet.2023.07.042","title":"Long-range zero-shot generative deep network quantization","year":2023,"lang":"en","type":"article","venue":"Neural Networks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Natural Science Foundation for Distinguished Young Scholars of Anhui Province; Natural Science Foundation of Anhui Province; Canadian Allergy, Asthma and Immunology Foundation","keywords":"Computer science; Quantization (signal processing); Deep learning; Algorithm; Inference; Artificial intelligence; Artificial neural network; Pattern recognition (psychology)","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":[0.0007351278,0.000575892,0.000895663,0.0004902096,0.0003741784,0.00106551,0.002229673,0.001374604,0.006989037],"category_scores_gemma":[0.003891285,0.0004812119,0.0005404189,0.0006819073,0.0009797072,0.001961045,0.002134991,0.002301998,0.001849465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001018487,"about_ca_system_score_gemma":0.0009831814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00461586,"about_ca_topic_score_gemma":0.010591,"domain_scores_codex":[0.9995282,0.0001135122,0.00002226279,0.0001315603,0.0001432303,0.00006132513],"domain_scores_gemma":[0.9989995,0.0004281035,0.00004550398,0.0002484223,0.0002137099,0.00006463214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002656775,0.0001156273,0.00104579,0.00017405,0.00006544728,0.0001302174,0.0001295216,0.5181693,0.006615894,0.1080401,0.01250982,0.3527387],"study_design_scores_gemma":[0.000005811905,0.00001351623,0.00006433368,0.000009279174,0.000004291115,0.00002196241,0.000006149318,0.9783426,0.0009074766,0.02007257,0.0005452308,0.000006738367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0127957,0.0005637042,0.9812252,0.0003530453,0.0001079856,0.00003141674,0.0002443358,0.001252511,0.003426073],"genre_scores_gemma":[0.7604987,0.0006206994,0.2186536,0.0006586731,0.0001450513,0.0001319913,0.001806048,0.0004693026,0.01701592],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006989037,"threshold_uncertainty_score":0.02338064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03483911809452436,"score_gpt":0.2849494091660054,"score_spread":0.250110291071481,"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."}}