{"id":"W2949908882","doi":"10.48550/arxiv.1808.03351","title":"Exploiting Structure for Fast Kernel Learning","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Computer science; Preconditioner; Scalability; Kernel (algebra); Kronecker delta; Gaussian process; Algorithm; Artificial intelligence; Machine learning; Theoretical computer science; Gaussian; Mathematics; Iterative method","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":[0.0001527792,0.0003532406,0.0003287935,0.0001859218,0.0003614614,0.0003422321,0.002139646,0.0003287667,0.00004967875],"category_scores_gemma":[0.00007656449,0.0003881881,0.0001924345,0.0003973823,0.00010675,0.0004740287,0.0018204,0.0006413156,0.00004005955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001104545,"about_ca_system_score_gemma":0.0002507465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002750944,"about_ca_topic_score_gemma":0.00001628473,"domain_scores_codex":[0.9978767,0.00006122416,0.0002059733,0.001275181,0.00009098842,0.0004899178],"domain_scores_gemma":[0.9982688,0.00009255268,0.0003664704,0.0008036749,0.000305365,0.000163144],"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":[0.00008013558,0.0001126557,0.01024451,0.001266088,0.0002750349,0.0002607728,0.002314608,0.1195283,0.0003480786,0.8479994,0.001516317,0.01605406],"study_design_scores_gemma":[0.0004964677,0.0001401743,0.0007316568,0.0002678846,0.00005653856,0.00001073882,0.0002096552,0.7255026,0.0006742595,0.2694205,0.001699456,0.0007900164],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09694333,0.00002942507,0.900139,0.00008052795,0.0005432999,0.0002499967,0.00002170489,0.0003041751,0.001688558],"genre_scores_gemma":[0.9823635,0.00003406988,0.01570323,0.000075391,0.0002666939,0.000001627782,0.00002151863,0.00002536178,0.001508633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8854201,"threshold_uncertainty_score":0.999857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05417632118871135,"score_gpt":0.1900920070355898,"score_spread":0.1359156858468785,"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."}}