{"id":"W2198190323","doi":"10.48550/arxiv.1510.03009","title":"Neural Networks with Few Multiplications","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"MNIST database; Computer science; Artificial neural network; Multiplication (music); Gradient descent; Computation; Point (geometry); Artificial intelligence; Layer (electronics); Stochastic gradient descent; Binary number; Sign (mathematics); Deep neural networks; Algorithm; Training (meteorology); Backpropagation; Arithmetic; Pattern recognition (psychology); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001091321,0.000356537,0.0002945691,0.0001441395,0.0002386088,0.0001193413,0.002491061,0.000219712,0.000005853082],"category_scores_gemma":[0.00001344189,0.0003689989,0.0001117191,0.001131804,0.0001651577,0.000439932,0.002009243,0.0007789666,0.00005634116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002108721,"about_ca_system_score_gemma":0.0001454463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003878117,"about_ca_topic_score_gemma":0.00007136309,"domain_scores_codex":[0.997856,0.00009115661,0.0001881974,0.00132952,0.0001017367,0.0004333643],"domain_scores_gemma":[0.9966123,0.000128786,0.0002993205,0.002314422,0.0003214917,0.000323671],"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":[0.00001150019,0.00004849129,0.0007654321,0.000006435055,0.00002652039,0.00004651157,0.00003703437,0.9207521,0.000002314079,0.076619,0.0007233628,0.000961279],"study_design_scores_gemma":[0.0003268121,0.00003714513,0.0006581936,0.0000232346,0.00004049833,0.00001240859,0.00001295485,0.9779544,0.000006822113,0.01930594,0.001198197,0.0004233849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02433117,0.0001068615,0.9722124,0.0004316624,0.0002170087,0.0005761709,0.00001009181,0.0006430058,0.001471667],"genre_scores_gemma":[0.983613,0.00008286302,0.01493766,0.000190285,0.0001373769,0.00001193273,0.00004160866,0.00003014307,0.0009550999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9592819,"threshold_uncertainty_score":0.9998762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08012054912875996,"score_gpt":0.2036987402104443,"score_spread":0.1235781910816844,"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."}}