{"id":"W44815768","doi":"10.1007/978-3-642-35289-8_32","title":"A Practical Guide to Training Restricted Boltzmann Machines","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":2825,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Boltzmann machine; Computer science; Set (abstract data type); Artificial intelligence; Generative grammar; Training set; Machine learning; Divergence (linguistics); 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":[0.0006321082,0.001450616,0.0009137404,0.0009324903,0.0004568992,0.001624658,0.002137626,0.00190817,0.09129911],"category_scores_gemma":[0.005805432,0.001312628,0.0008387299,0.001625109,0.0006641716,0.00191142,0.001558134,0.003688617,0.04879988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007184062,"about_ca_system_score_gemma":0.0009189467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00219469,"about_ca_topic_score_gemma":0.00729706,"domain_scores_codex":[0.9995335,0.0001108045,0.00004318441,0.00008380997,0.0001943629,0.00003439968],"domain_scores_gemma":[0.9988212,0.0007339102,0.00002575929,0.000193758,0.000196719,0.00002869502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005346482,0.000076926,0.0001981566,0.0006515492,0.00004920475,0.0001664731,0.0001007929,0.05869308,0.00458409,0.1678696,0.1776134,0.5899433],"study_design_scores_gemma":[0.00006188035,0.0000408737,0.0002405837,0.0003299709,0.00002657003,0.0004026153,0.00004870895,0.2478699,0.004902196,0.4009129,0.3451011,0.00006284751],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002850903,0.001745084,0.9766506,0.0003577392,0.0002242662,0.00005463877,0.0008534369,0.003321102,0.01650815],"genre_scores_gemma":[0.008183428,0.002508205,0.932865,0.000580117,0.0002258572,0.0005607014,0.00200771,0.002388784,0.05068011],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09129911,"threshold_uncertainty_score":0.305426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03733683725891546,"score_gpt":0.2967167350036891,"score_spread":0.2593798977447736,"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."}}