{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001676011,0.0006272974,0.000700415,0.0006312861,0.000393779,0.001044624,0.0009328995,0.0009794029,0.001747789],"category_scores_gemma":[0.009560085,0.0003389803,0.0007101342,0.0006232265,0.001222655,0.002199874,0.001680842,0.00199192,0.0005057763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001158986,"about_ca_system_score_gemma":0.0009292579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003221412,"about_ca_topic_score_gemma":0.003172393,"domain_scores_codex":[0.9994549,0.0001881244,0.00002578129,0.000134056,0.0001460682,0.00005109098],"domain_scores_gemma":[0.9982411,0.0009364575,0.0001544555,0.0003083205,0.0002682347,0.00009147837],"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":[0.0001107067,0.00005293097,0.001561013,0.0001301948,0.00004760261,0.00009577424,0.0001687353,0.5966153,0.005162572,0.2826063,0.001518363,0.1119305],"study_design_scores_gemma":[0.000002164832,0.00000613923,0.00009612268,0.000003938478,0.000002135233,0.000009266203,0.000003858244,0.9520828,0.0003202507,0.04722154,0.0002483735,0.000003382195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01158667,0.0001810232,0.9870733,0.0001522767,0.00001223543,0.00001187632,0.00003145682,0.0001784444,0.0007726814],"genre_scores_gemma":[0.6905538,0.0008001732,0.3036909,0.0001562563,0.00007149906,0.0001015554,0.0003362206,0.0001854889,0.004104045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003221412,"threshold_uncertainty_score":0.008863688,"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."}}