{"id":"W2125113755","doi":"","title":"A Better Way to Pretrain Deep Boltzmann Machines","year":2012,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"MNIST database; Boltzmann machine; Computer science; Restricted Boltzmann machine; Artificial intelligence; Deep belief network; Deep learning; Layer (electronics); Boltzmann constant; Generative grammar; Generative model; Pattern recognition (psychology); Machine learning; Algorithm","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":[0.001393595,0.001314857,0.001252665,0.0005911745,0.000583781,0.001679962,0.002270007,0.002457886,0.009108966],"category_scores_gemma":[0.008306087,0.001152521,0.001003023,0.000878687,0.001027375,0.004190531,0.001626796,0.006718304,0.002348166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000859594,"about_ca_system_score_gemma":0.001078169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002600589,"about_ca_topic_score_gemma":0.004901662,"domain_scores_codex":[0.9990076,0.0002971799,0.00006234398,0.0003225069,0.0002206741,0.00008972998],"domain_scores_gemma":[0.9985445,0.000618198,0.00007748709,0.0004386909,0.0002579779,0.00006320603],"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":[0.0001460074,0.0001787261,0.0008576712,0.0001292157,0.0001650964,0.0001082499,0.0001607724,0.6840776,0.01746992,0.07791934,0.004980077,0.2138074],"study_design_scores_gemma":[0.00002684596,0.00003384878,0.0001166794,0.00002244973,0.00001318286,0.00004843387,0.00001238278,0.9562456,0.008004503,0.03157466,0.003878316,0.00002301681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003087217,0.0001060043,0.9947078,0.00033721,0.00007681258,0.00002283276,0.00003767011,0.0007484208,0.0008759311],"genre_scores_gemma":[0.1482359,0.0002889789,0.8427349,0.0008437572,0.0001319415,0.0002516015,0.0004321087,0.0009381202,0.006142731],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009108966,"threshold_uncertainty_score":0.03047252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0145427736631149,"score_gpt":0.2387916199599926,"score_spread":0.2242488462968777,"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."}}