{"id":"W2187720631","doi":"10.1109/idaacs.2015.7340725","title":"A new technique for restricted Boltzmann machine learning","year":2015,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"MNIST database; Boltzmann machine; Restricted Boltzmann machine; Artificial intelligence; Computer science; Deep belief network; Perceptron; Artificial neural network; Deep learning; Machine learning; Visualization; Multilayer perceptron; Representation (politics); Pattern recognition (psychology)","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":[0.001552607,0.0008557356,0.0009718747,0.0005942936,0.0003821383,0.000793919,0.001872844,0.001479033,0.004080066],"category_scores_gemma":[0.004517186,0.0006048788,0.001077534,0.0006933213,0.001739172,0.001708546,0.002411244,0.003295458,0.001220068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006321613,"about_ca_system_score_gemma":0.0005948396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000983648,"about_ca_topic_score_gemma":0.0008579367,"domain_scores_codex":[0.9989256,0.0004772713,0.00005241955,0.0001820297,0.0002955569,0.00006713187],"domain_scores_gemma":[0.9990616,0.0005342534,0.00007818716,0.0001598751,0.0001223342,0.00004384418],"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":[0.00008885771,0.00004159081,0.0005660637,0.0002053183,0.0001215239,0.0001237904,0.0001476713,0.5455598,0.009107979,0.3164656,0.004415242,0.1231567],"study_design_scores_gemma":[0.000008469709,0.00002925921,0.00006395555,0.0000198136,0.0000097477,0.00007802144,0.000005874399,0.9298011,0.001754017,0.06338042,0.004835761,0.00001358284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008334252,0.0001735577,0.9974915,0.0001541621,0.00004977186,0.0000160389,0.00001784577,0.0001586139,0.001105067],"genre_scores_gemma":[0.2810026,0.001111348,0.7008247,0.0008995081,0.0003578204,0.0004650158,0.0002860628,0.0005257613,0.0145271],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004080066,"threshold_uncertainty_score":0.01364917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03087663653453267,"score_gpt":0.2543824357478542,"score_spread":0.2235057992133215,"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."}}