{"id":"W2115595010","doi":"10.48550/arxiv.1312.6110","title":"Learning Generative Models with Visual Attention","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Generative model; Computer science; Generative grammar; Artificial intelligence; Inference; Machine learning","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":[0.001265051,0.001173656,0.001199415,0.001111417,0.0004487275,0.001277101,0.001635246,0.00187995,0.004490767],"category_scores_gemma":[0.005821599,0.001035085,0.001700559,0.0008681022,0.00172554,0.001467824,0.00203152,0.002404242,0.0007743945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001556341,"about_ca_system_score_gemma":0.0006495676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005628141,"about_ca_topic_score_gemma":0.007899488,"domain_scores_codex":[0.999368,0.0002576363,0.00002223516,0.0001740317,0.0001095426,0.00006857879],"domain_scores_gemma":[0.9975702,0.001913278,0.0001632121,0.0001615884,0.000114184,0.00007760804],"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":[0.00003339989,0.00001736246,0.0005511322,0.00004655407,0.00005813818,0.00008106013,0.00008312919,0.8682861,0.0006314698,0.1117926,0.001727133,0.01669176],"study_design_scores_gemma":[0.000006341891,0.000005347451,0.00006081568,0.000006139208,0.00000586952,0.00001651619,0.000004226674,0.9436717,0.00009490758,0.05569744,0.0004257601,0.000005052562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01215621,0.0006131423,0.9822513,0.0007361757,0.00007102513,0.00003471247,0.0002147114,0.0003979848,0.003524627],"genre_scores_gemma":[0.789077,0.001444837,0.1824809,0.001060637,0.0004816463,0.0005374224,0.001092818,0.0004876046,0.02333714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005628141,"threshold_uncertainty_score":0.01502311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05209607952777962,"score_gpt":0.1778900880922505,"score_spread":0.1257940085644709,"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."}}