{"id":"W2162747531","doi":"","title":"Generative versus discriminative training of RBMs for classification of fMRI images","year":2008,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"","keywords":"Discriminative model; Overfitting; Artificial intelligence; Computer science; Pattern recognition (psychology); Generative model; Voxel; Machine learning; Set (abstract data type); Training set; Data set; Task (project management); Generative grammar; 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":[0.003092187,0.001071206,0.000939609,0.0005335102,0.000284624,0.0005881837,0.001545475,0.001427628,0.001735673],"category_scores_gemma":[0.009664241,0.0006246325,0.0006440149,0.0005257968,0.0016901,0.001478181,0.001474696,0.002038325,0.0004918594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009266519,"about_ca_system_score_gemma":0.0005052624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001687563,"about_ca_topic_score_gemma":0.002821132,"domain_scores_codex":[0.9990946,0.000508167,0.00003137156,0.0001833401,0.00009371625,0.0000888663],"domain_scores_gemma":[0.9961905,0.002640043,0.0002712902,0.0006535572,0.0001384066,0.0001062883],"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":[0.0002950456,0.0001000019,0.001194776,0.00009564042,0.00005767346,0.00008006563,0.0001088254,0.8981409,0.006980653,0.01599142,0.001012045,0.075943],"study_design_scores_gemma":[0.000009789572,0.00003181574,0.0002157149,0.000007050142,0.000007116368,0.00002202486,0.000005448998,0.9908906,0.00167938,0.00697378,0.0001509181,0.000006413854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09179756,0.0006581341,0.9025804,0.0009304096,0.00004504127,0.00008961321,0.0001281299,0.001142376,0.002628367],"genre_scores_gemma":[0.887952,0.0003122647,0.107523,0.000404306,0.0000697208,0.0001249715,0.0003143095,0.0001956273,0.003103783],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003092187,"threshold_uncertainty_score":0.01635325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1259106268912469,"score_gpt":0.297985180630316,"score_spread":0.1720745537390691,"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."}}