{"id":"W2084336274","doi":"10.1111/cogs.12049","title":"Where Do Features Come From?","year":2013,"lang":"en","type":"article","venue":"Cognitive Science","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Boltzmann machine; Computer science; Backpropagation; Artificial intelligence; Restricted Boltzmann machine; Artificial neural network; Initialization; Feed forward; Generalization; Deep learning; Deep belief network; Machine learning; Feedforward neural network; Generative model; Set (abstract data type); Inference; Generative grammar; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001268287,0.0008501468,0.0009610539,0.001771548,0.001161752,0.004908369,0.001181934,0.0018902,0.03130246],"category_scores_gemma":[0.01195423,0.0006855949,0.0009266275,0.001963289,0.003127629,0.01445056,0.001653393,0.002772846,0.01405996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001440102,"about_ca_system_score_gemma":0.001164084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003659565,"about_ca_topic_score_gemma":0.003030282,"domain_scores_codex":[0.9987364,0.0002209008,0.00005358467,0.0006099819,0.0002280464,0.0001510852],"domain_scores_gemma":[0.9975793,0.0009988146,0.0003585493,0.0005015911,0.0004102348,0.0001514644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005334918,0.0001255532,0.03365628,0.001151582,0.0003470001,0.001321053,0.002580306,0.002411131,0.005918778,0.3897025,0.1053039,0.4569484],"study_design_scores_gemma":[0.00003646907,0.00005340385,0.01810241,0.0006747728,0.0001430252,0.001370477,0.00236225,0.006378819,0.004616976,0.7171295,0.2490208,0.0001111348],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1171683,0.02581345,0.5109851,0.09165916,0.005517302,0.0003481059,0.01998202,0.004489623,0.2240369],"genre_scores_gemma":[0.8419904,0.009592013,0.07212584,0.01108887,0.001468363,0.0003019737,0.008659256,0.002309224,0.05246413],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03130246,"threshold_uncertainty_score":0.1047171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01050165385466765,"score_gpt":0.2369419201747493,"score_spread":0.2264402663200817,"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."}}