{"id":"W2963827215","doi":"","title":"Differentiable Compositional Kernel Learning for Gaussian Processes","year":2018,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Kernel (algebra); Extrapolation; Differentiable function; Generalization; Computer science; Artificial intelligence; Artificial neural network; Gaussian process; Mathematics; Gaussian; Algorithm; Pattern recognition (psychology); Discrete mathematics; Statistics; Pure mathematics; Mathematical analysis","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":[0.0002323055,0.0002804486,0.0002276183,0.0002324147,0.0005789913,0.0007674898,0.001301064,0.00008956126,0.0007076408],"category_scores_gemma":[0.0003417601,0.0002533354,0.00008458924,0.0002901205,0.0001164896,0.0006059737,0.0002399075,0.0004934035,0.000228766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006182768,"about_ca_system_score_gemma":0.0002178491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003232285,"about_ca_topic_score_gemma":0.00002555959,"domain_scores_codex":[0.997983,0.00007448754,0.0003385438,0.000660712,0.0005297249,0.0004135632],"domain_scores_gemma":[0.9983352,0.0001857048,0.000263918,0.0002177422,0.0008640552,0.0001333164],"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":[0.0001402083,0.000202723,0.01044305,0.000102729,0.00009731917,0.00001002294,0.0007004408,0.0006117923,0.002576338,0.9650436,0.0004501514,0.01962161],"study_design_scores_gemma":[0.001294876,0.00130585,0.005117195,0.0003830177,0.00001653973,0.00005715833,0.0000964088,0.8960921,0.004940758,0.05652535,0.03345455,0.0007162661],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009447845,0.00004169079,0.9332802,0.005216806,0.0005264421,0.000219639,0.00001444547,0.0003854927,0.05086739],"genre_scores_gemma":[0.9773408,0.0000342148,0.01661914,0.0004089843,0.0004158416,0.00006561954,0.0001071335,0.00002362974,0.004984644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9678929,"threshold_uncertainty_score":0.9999919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02641541766885436,"score_gpt":0.2961644482254642,"score_spread":0.2697490305566098,"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."}}