{"id":"W2013035813","doi":"10.1162/neco_a_00142","title":"A Connection Between Score Matching and Denoising Autoencoders","year":2011,"lang":"en","type":"article","venue":"Neural Computation","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":980,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Autoencoder; Restricted Boltzmann machine; Artificial intelligence; Noise reduction; Pattern recognition (psychology); Estimator; Computer science; Matching (statistics); Probabilistic logic; Unsupervised learning; Nonparametric statistics; Energy (signal processing); Rank (graph theory); Artificial neural network; Machine learning; Mathematics; Statistics","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":[0.00434522,0.0008820074,0.001427061,0.001094324,0.0006144848,0.002333482,0.002337994,0.002718036,0.005138733],"category_scores_gemma":[0.02301117,0.0007314727,0.0009318086,0.001318512,0.004077028,0.004746155,0.003700253,0.003950211,0.0009516806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001190523,"about_ca_system_score_gemma":0.0009703642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001175254,"about_ca_topic_score_gemma":0.001311521,"domain_scores_codex":[0.9968197,0.001136057,0.0001743124,0.0007129708,0.000935832,0.000221133],"domain_scores_gemma":[0.9928831,0.004201463,0.0006440391,0.00123565,0.0007290904,0.0003066519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008107982,0.00006399095,0.0009618569,0.00009230774,0.00006930146,0.00008502133,0.0001361996,0.1112392,0.003446806,0.8160703,0.00134924,0.06640466],"study_design_scores_gemma":[0.00001690273,0.00005889947,0.0003650503,0.00002606087,0.00001329252,0.00009779067,0.00001932998,0.4221435,0.001741351,0.5735407,0.001949693,0.00002748476],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.011566,0.0003127719,0.9815393,0.0005724918,0.00004934218,0.00003130257,0.00003507685,0.0001449661,0.005748769],"genre_scores_gemma":[0.6593494,0.0008360638,0.3245036,0.0007902996,0.0003233157,0.0001921604,0.0002212599,0.0003999001,0.01338402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005138733,"threshold_uncertainty_score":0.02298003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06044979363662861,"score_gpt":0.2499212846951548,"score_spread":0.1894714910585262,"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."}}