{"id":"W1531103298","doi":"10.48550/arxiv.1409.2752","title":"Winner-Take-All Autoencoders","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":174,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Natural language processing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001511251,0.0003727423,0.0003383567,0.0001933691,0.0001836019,0.0001091346,0.003232589,0.0002814265,0.00002180468],"category_scores_gemma":[0.00002405127,0.0004441928,0.0002177731,0.0006278133,0.0001437734,0.0003616959,0.002417952,0.0007274667,0.0003680762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002034458,"about_ca_system_score_gemma":0.000117484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003481699,"about_ca_topic_score_gemma":0.00002584684,"domain_scores_codex":[0.9975525,0.0001177067,0.0002221615,0.001504315,0.0001086728,0.0004946779],"domain_scores_gemma":[0.9968166,0.0001588155,0.0003186553,0.002330144,0.0001233974,0.0002524252],"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":[0.00000426773,0.00003535588,0.0001395774,0.00001849573,0.00003347676,0.0000520245,0.00005847302,0.7046669,0.00002001465,0.2920034,0.001982862,0.0009852012],"study_design_scores_gemma":[0.0002126523,0.00002437842,0.0002519836,0.00003067345,0.00003540246,0.000004972419,0.000008486933,0.8322447,0.00004853814,0.1495374,0.01713959,0.0004612408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01576835,0.00003993549,0.9762499,0.0006240301,0.0005438222,0.00034359,0.000006898641,0.0007687993,0.005654689],"genre_scores_gemma":[0.973793,0.0001260171,0.02273697,0.0006729514,0.0001464988,0.000003597126,0.00002163965,0.00002863912,0.002470735],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9580246,"threshold_uncertainty_score":0.999801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07883050987738648,"score_gpt":0.2015105177524568,"score_spread":0.1226800078750703,"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."}}