{"id":"W1665214252","doi":"","title":"Rectified Linear Units Improve Restricted Boltzmann Machines","year":2010,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":13244,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Boltzmann machine; Binary number; Sigmoid function; Computer science; Object (grammar); Restricted Boltzmann machine; Artificial intelligence; Inference; Pattern recognition (psychology); Feature (linguistics); Cognitive neuroscience of visual object recognition; Algorithm; Mathematics; Computer vision; Deep learning; Arithmetic; Artificial neural network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001184574,0.001141091,0.001535658,0.0004816662,0.0002770543,0.0008527474,0.002686423,0.001523975,0.006606211],"category_scores_gemma":[0.004999585,0.0008760685,0.001140939,0.0005322052,0.0008452683,0.002484872,0.001955863,0.002776678,0.002912994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007434928,"about_ca_system_score_gemma":0.0006569138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002203554,"about_ca_topic_score_gemma":0.002299702,"domain_scores_codex":[0.9992726,0.0001938437,0.00004524475,0.0002033851,0.0001938301,0.00009104572],"domain_scores_gemma":[0.9986261,0.0006573098,0.0001297861,0.0003000131,0.0002298637,0.00005700101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001484445,0.00007989147,0.000368219,0.0001387986,0.0001353515,0.00006003154,0.00005816192,0.8029372,0.005183906,0.04040131,0.003779845,0.1467089],"study_design_scores_gemma":[0.00001013054,0.0000253549,0.00004659127,0.000007128492,0.000009051601,0.00001390948,0.000002821071,0.9856435,0.001231187,0.0121743,0.0008282075,0.000007768767],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01737024,0.0009450199,0.9741785,0.0003110991,0.0001872178,0.00005561122,0.000112605,0.002443991,0.004395715],"genre_scores_gemma":[0.6282246,0.001042611,0.3439321,0.000694205,0.0002638404,0.0002915338,0.0006990057,0.0008741973,0.02397791],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006606211,"threshold_uncertainty_score":0.02210003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03053323629475789,"score_gpt":0.2802265611432088,"score_spread":0.2496933248484509,"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."}}