{"id":"W2398823745","doi":"","title":"A Generative Process for Contractive Auto-Encoders.","year":2012,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Process (computing); Generative grammar; Artificial intelligence; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0003456335,0.0001418573,0.0001303813,0.0001340374,0.000181827,0.0001700959,0.0005765414,0.000054245,0.00006551972],"category_scores_gemma":[0.0002806457,0.0001309033,0.00005645919,0.0001000589,0.00002507503,0.0005631443,0.00007488852,0.0002776749,0.00002560499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004979713,"about_ca_system_score_gemma":0.00006318236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001338817,"about_ca_topic_score_gemma":0.000001385991,"domain_scores_codex":[0.9989663,0.00008764117,0.000180251,0.0002620337,0.0002607438,0.0002430249],"domain_scores_gemma":[0.9991067,0.0001547421,0.0001742668,0.0001243972,0.0003618855,0.00007798348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008955638,0.0002531403,0.0045325,0.00001268914,0.0001096402,0.000002811861,0.00408162,0.0440412,0.001215514,0.9049813,0.0005509452,0.04012905],"study_design_scores_gemma":[0.0003210395,0.0001203168,0.000304784,0.00002720662,0.000003043524,0.000006894106,0.0000468986,0.9862464,0.002503628,0.00624557,0.004001239,0.0001729655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00126605,0.00004996311,0.9716182,0.002551255,0.0003648917,0.0001792548,0.000005352551,0.000364965,0.02360009],"genre_scores_gemma":[0.9349989,0.00001734193,0.06322317,0.0005388831,0.0001742622,0.00006815662,0.00002731223,0.000009932876,0.0009420566],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9422052,"threshold_uncertainty_score":0.533808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.057069992015881,"score_gpt":0.3567650522907404,"score_spread":0.2996950602748594,"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."}}