{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001670608,0.000370022,0.0003445787,0.0001753183,0.000307627,0.0003255966,0.0008967029,0.0002180666,0.00003856607],"category_scores_gemma":[0.00001127552,0.0003525755,0.0001645566,0.0003963925,0.0001068811,0.001086509,0.001207244,0.0006254631,0.00009618897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001405262,"about_ca_system_score_gemma":0.0001424039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002150663,"about_ca_topic_score_gemma":0.00003095411,"domain_scores_codex":[0.9978489,0.0003242316,0.0001655651,0.001164984,0.0001212469,0.0003751131],"domain_scores_gemma":[0.9986102,0.00006088377,0.0002731377,0.0005788831,0.0003216516,0.00015525],"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":[0.00001358111,0.00004636077,0.0003045462,0.0000124438,0.0001170278,0.00006265967,0.0001959004,0.9661555,0.0001346228,0.03156773,0.0001687988,0.00122079],"study_design_scores_gemma":[0.0003045059,0.0001447451,0.0002743168,0.00006246215,0.00005541123,0.000002539928,0.0001031398,0.9834656,0.0002277564,0.01482077,0.00009308581,0.0004457198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1356164,0.00003556387,0.861473,0.00008239714,0.0002573884,0.0002720478,0.000001965878,0.0001721864,0.002089017],"genre_scores_gemma":[0.9792134,0.0001057123,0.01678433,0.00006390011,0.0001626537,0.000003040646,0.00001942168,0.00002278313,0.003624729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8446887,"threshold_uncertainty_score":0.9998927,"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."}}