{"id":"W3081341002","doi":"10.1109/mis.2022.3169036","title":"Fast Approximate Multioutput Gaussian Processes","year":2022,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gaussian process; Eigenvalues and eigenvectors; Hyperparameter; Covariance matrix; Covariance; Kernel (algebra); Mathematics; Kriging; Algorithm; Computational complexity theory; Inverse; Gaussian; Kernel method; Applied mathematics; Artificial intelligence; Computer science; Machine learning; Statistics; Discrete mathematics; Support vector machine","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":[0.001156201,0.0007409059,0.0009331941,0.0005362927,0.0003675412,0.001083236,0.00115205,0.001018466,0.002133848],"category_scores_gemma":[0.004116667,0.0004855604,0.0007961281,0.0007497682,0.0006332259,0.001260915,0.001078678,0.001586398,0.000887991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008488722,"about_ca_system_score_gemma":0.0008969756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005273069,"about_ca_topic_score_gemma":0.004811387,"domain_scores_codex":[0.9993592,0.0001834467,0.0000262612,0.0001343255,0.0002377752,0.00005902581],"domain_scores_gemma":[0.9989138,0.0005672287,0.00009597318,0.0001815042,0.0002112787,0.00003026943],"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":[0.00009742595,0.00003336292,0.0006557211,0.00007028318,0.00003352849,0.00007598439,0.00008044371,0.8601708,0.004075554,0.03877004,0.001143309,0.09479361],"study_design_scores_gemma":[0.000001739247,0.000003167286,0.00003925315,0.000001806418,0.000001282732,0.000006921426,0.000001800024,0.9967459,0.0004027211,0.002540317,0.0002532891,0.000001815287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004671587,0.00008994668,0.9940556,0.00006562834,0.00001395724,0.00001093648,0.00002460319,0.0003238801,0.0007438499],"genre_scores_gemma":[0.4798688,0.0004334758,0.5130766,0.0001332921,0.00006751078,0.0001317221,0.0002583243,0.0002350361,0.005795197],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005273069,"threshold_uncertainty_score":0.01048476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0239675156936359,"score_gpt":0.2454106282888358,"score_spread":0.2214431125951999,"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."}}