{"id":"W2136836265","doi":"","title":"Adaptive dropout for training deep neural networks","year":2013,"lang":"en","type":"article","venue":"neural information processing systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":280,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Dropout (neural networks); MNIST database; Computer science; Artificial intelligence; Artificial neural network; Feature (linguistics); Boltzmann machine; Deep belief network; Restricted Boltzmann machine; Stochastic gradient descent; Pattern recognition (psychology); Convolutional neural network; Machine learning; Gradient descent; Deep learning; Backpropagation","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":[0.002532988,0.00163039,0.001390848,0.0007825632,0.0004931596,0.0007281076,0.002410142,0.001743993,0.002599454],"category_scores_gemma":[0.009215246,0.0008076783,0.0008850672,0.001163765,0.0008317434,0.001702418,0.001184674,0.002890878,0.0009752566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00208935,"about_ca_system_score_gemma":0.00138793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007458387,"about_ca_topic_score_gemma":0.009496566,"domain_scores_codex":[0.9990265,0.0003035218,0.00007445418,0.0001907642,0.0003011811,0.0001035908],"domain_scores_gemma":[0.9981797,0.001068271,0.0001724509,0.0002107065,0.0003099995,0.00005899304],"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":[0.0001855017,0.00009602705,0.001215815,0.0002279037,0.000106024,0.0001091253,0.00007202156,0.7541853,0.003640156,0.01371893,0.005606814,0.2208365],"study_design_scores_gemma":[0.000008567491,0.00001251579,0.00008281985,0.000009818968,0.000004526226,0.000009035216,0.000002355215,0.993558,0.001087368,0.004687907,0.00053352,0.000003544253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007589848,0.0007353236,0.9880284,0.0002754466,0.00005882711,0.00005295054,0.0001704069,0.002399314,0.0006894648],"genre_scores_gemma":[0.4366782,0.001072215,0.5525709,0.000482965,0.0001572557,0.0005980167,0.001540378,0.0006039467,0.006296165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007458387,"threshold_uncertainty_score":0.01515937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03198876176642353,"score_gpt":0.2623541483614051,"score_spread":0.2303653865949816,"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."}}