{"id":"W2211622316","doi":"10.1109/iccv.2015.289","title":"Contractive Rectifier Networks for Nonlinear Maximum Margin Classification","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"MNIST database; Margin (machine learning); Nonlinear system; Support vector machine; Rectifier (neural networks); Contraction (grammar); Computer science; Mathematical optimization; Mathematics; Algorithm; Control theory (sociology); Artificial intelligence; Machine learning; Artificial neural network; Recurrent neural network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001136251,0.0000784002,0.00008754306,0.00002026536,0.00003425183,0.00001339962,0.00005001521,0.00005299958,0.000008773302],"category_scores_gemma":[0.00004392617,0.0000729964,0.00002752541,0.00006772196,0.00000904135,0.0001239317,0.000007462115,0.0001062452,0.00001276152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004318243,"about_ca_system_score_gemma":0.000006481715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.539436e-7,"about_ca_topic_score_gemma":0.000003139369,"domain_scores_codex":[0.9995714,0.00000905362,0.0001102118,0.0001084057,0.00004385133,0.0001571128],"domain_scores_gemma":[0.9996565,0.0001035402,0.00001878986,0.00009612241,0.00005798921,0.00006708255],"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":[0.0001987134,0.00003866838,0.00008814498,0.00003458227,0.00004980014,0.000004652571,0.0002031816,0.7932786,0.012863,0.002130695,0.0141096,0.1770004],"study_design_scores_gemma":[0.0004123155,0.00003475082,0.00010531,0.000006966342,0.000006651616,0.000004417454,0.000142192,0.9542478,0.01189581,0.001306732,0.0317058,0.0001312617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03311488,0.0001047159,0.9555062,0.0001276068,0.0008296628,0.0002820212,0.000003395132,0.0003908388,0.009640697],"genre_scores_gemma":[0.9794132,0.00001104045,0.0191554,0.0001041393,0.0004935941,0.00002994504,0.00002515032,0.00002924722,0.0007383514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9462982,"threshold_uncertainty_score":0.2976707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05858663890322559,"score_gpt":0.2797400539479395,"score_spread":0.2211534150447139,"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."}}