{"id":"W2390241580","doi":"","title":"Sequential Inference for Deep Gaussian Process","year":2016,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; University of Toronto","funders":"","keywords":"Gaussian process; Inference; Computer science; Process (computing); Artificial intelligence; Machine learning; Gaussian","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":[0.007861573,0.001546376,0.003303397,0.001599604,0.001139444,0.002069169,0.003534705,0.002318736,0.006913843],"category_scores_gemma":[0.02795172,0.002677065,0.002208447,0.001691211,0.002251614,0.004472518,0.003266372,0.005845265,0.0009010505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002591051,"about_ca_system_score_gemma":0.004842194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02498458,"about_ca_topic_score_gemma":0.02983041,"domain_scores_codex":[0.9979764,0.0007680492,0.0001530059,0.0005095548,0.0003815046,0.0002115183],"domain_scores_gemma":[0.9812236,0.01554084,0.0006282696,0.001017697,0.001137396,0.0004521628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004144254,0.0001622226,0.001899953,0.0002477243,0.0003141554,0.0001158672,0.0001778777,0.6986884,0.001313875,0.1867652,0.004441419,0.1054589],"study_design_scores_gemma":[0.00002139096,0.00001102406,0.0001050214,0.00000900815,0.00001465093,0.000008374479,0.000004818433,0.9247795,0.0001824363,0.0745348,0.0003209522,0.00000808848],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008155867,0.0003826982,0.9899651,0.0004261889,0.00007412148,0.00002703194,0.0001051766,0.0002791128,0.0005847735],"genre_scores_gemma":[0.5129784,0.0014184,0.4699017,0.0007062839,0.0005971861,0.0004061493,0.001589025,0.0005826218,0.01182016],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02498458,"threshold_uncertainty_score":0.04967827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08939333074622631,"score_gpt":0.3665205894727141,"score_spread":0.2771272587264878,"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."}}