{"id":"W2106439909","doi":"10.1162/neco_a_00158","title":"Quickly Generating Representative Samples from an RBM-Derived Process","year":2011,"lang":"en","type":"article","venue":"Neural Computation","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Gibbs sampling; Herding; Sampling (signal processing); Exploit; Markov chain; Computer science; Algorithm; Markov process; Mixing (physics); Process (computing); Divergence (linguistics); Mathematics; Artificial intelligence; Machine learning; Statistics; Bayesian probability","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001202774,0.0001683334,0.0001710311,0.00005907003,0.0002535602,0.0002031858,0.0004247349,0.00004608326,0.00003222829],"category_scores_gemma":[0.00005472661,0.000154996,0.00004956525,0.0002456398,0.00004686804,0.001445499,0.0001003303,0.0001083004,0.00001575385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001887207,"about_ca_system_score_gemma":0.00003497525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006419101,"about_ca_topic_score_gemma":0.00006927308,"domain_scores_codex":[0.9984452,0.0002957783,0.0002603435,0.0005486397,0.000225818,0.0002242335],"domain_scores_gemma":[0.9991523,0.0001139168,0.0001736568,0.000258424,0.0002031092,0.00009858802],"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":[0.00009304521,0.0003724654,0.005753937,0.0000212787,0.0001301739,0.00006028235,0.06410374,0.4470319,0.1337053,0.001885829,0.0008826722,0.3459593],"study_design_scores_gemma":[0.0002258361,0.0001303954,0.01859046,0.000007175554,0.000008465119,0.000002767364,0.0003374083,0.9380725,0.03729144,0.005139416,0.000005359495,0.0001887368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3969911,0.0000207765,0.6022308,0.00009167354,0.000254826,0.0001133716,0.000003497633,0.0001160354,0.0001779143],"genre_scores_gemma":[0.7861745,0.000001646188,0.2132137,0.0003341569,0.0002137687,0.00001261721,0.00003228824,0.00001035181,0.000006953385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4910406,"threshold_uncertainty_score":0.6320554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07464532759247174,"score_gpt":0.3044249065450997,"score_spread":0.229779578952628,"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."}}