{"id":"W1507070875","doi":"10.48550/arxiv.1206.4635","title":"Deep Mixtures of Factor Analysers","year":2012,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Overfitting; Latent variable; Computer science; Layer (electronics); Factor (programming language); Graphical model; Artificial intelligence; Boltzmann machine; Machine learning; Deep learning; Variety (cybernetics); Restricted Boltzmann machine; Inference; Artificial neural network; Chemistry","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":[0.003048797,0.001718519,0.001689579,0.001600375,0.0006163911,0.002038289,0.002279914,0.001865166,0.006995136],"category_scores_gemma":[0.01347033,0.001280359,0.003405081,0.001428278,0.001647092,0.004247998,0.002880328,0.003741915,0.002435356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001553706,"about_ca_system_score_gemma":0.001245489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006034955,"about_ca_topic_score_gemma":0.008022066,"domain_scores_codex":[0.9977815,0.0009190155,0.0001295574,0.0005643836,0.0004076825,0.000198033],"domain_scores_gemma":[0.9965358,0.001976065,0.0003612246,0.0005354291,0.0004453242,0.0001460743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005031512,0.0001218913,0.003891391,0.0002165687,0.0004051803,0.0001811716,0.000414693,0.4102223,0.007525904,0.1958247,0.00869852,0.3719945],"study_design_scores_gemma":[0.00001344144,0.00002301088,0.0002120516,0.00001559785,0.00002351316,0.00004551656,0.00001715865,0.9062925,0.001437161,0.09006363,0.001835785,0.00002061549],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004016452,0.0001637577,0.9940824,0.0001129617,0.00002304565,0.00002443507,0.0001210188,0.0009476145,0.0005083758],"genre_scores_gemma":[0.2772799,0.0004221862,0.7132052,0.0003301535,0.0001286579,0.0002389397,0.001020712,0.0005917492,0.00678261],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006995136,"threshold_uncertainty_score":0.02340108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04871719549440465,"score_gpt":0.1822482766019795,"score_spread":0.1335310811075748,"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."}}