{"id":"W3134848548","doi":"10.1109/mdat.2021.3063356","title":"Training Binarized Neural Networks Using Ternary Multipliers","year":2021,"lang":"en","type":"article","venue":"IEEE Design and Test","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Ternary operation; Artificial intelligence; Computer science; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009977678,0.000106019,0.0001202099,0.00002536473,0.0002150108,0.0001839794,0.0002067991,0.00004405381,0.000003103342],"category_scores_gemma":[0.00001808121,0.00009970892,0.00003569663,0.0002917811,0.00003952221,0.0001822611,0.00006243292,0.0001274782,0.000001820419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009231253,"about_ca_system_score_gemma":0.00003096971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006244421,"about_ca_topic_score_gemma":9.27066e-7,"domain_scores_codex":[0.9991989,0.00004901888,0.0001309467,0.0003053439,0.00007937985,0.0002364514],"domain_scores_gemma":[0.9991555,0.0004255558,0.00004515352,0.000238035,0.00003176408,0.000103969],"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":[0.00001787924,0.0002642264,0.002977007,0.00001798316,0.00005084672,0.0008126675,0.001196348,0.3642796,0.182582,0.00693841,0.001718234,0.4391448],"study_design_scores_gemma":[0.0002353357,0.0000250004,0.0003869047,0.00001245083,0.000006125847,0.0001452842,0.00001552084,0.9974934,0.0008129902,0.0004558433,0.0002839207,0.0001271613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04071804,0.000257967,0.9580966,0.0003747283,0.0002558569,0.0001086619,7.150257e-7,0.000103367,0.00008405376],"genre_scores_gemma":[0.9157197,0.00003075679,0.08348591,0.0005353033,0.0001424519,0.000009786486,0.000001234532,0.000008734193,0.00006609888],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8750017,"threshold_uncertainty_score":0.4066012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07940189450894619,"score_gpt":0.2739379118057563,"score_spread":0.1945360172968101,"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."}}