{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002415839,0.0002401404,0.0002004783,0.0001637394,0.0001811655,0.0005128012,0.0009908359,0.00009235807,0.0002885516],"category_scores_gemma":[0.0006426385,0.0001736695,0.00004320716,0.000151797,0.0002190498,0.0005438414,0.0001170503,0.0001108486,0.0001184573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004889447,"about_ca_system_score_gemma":0.0002251688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001119986,"about_ca_topic_score_gemma":0.00006833702,"domain_scores_codex":[0.9981113,0.00003954643,0.000475523,0.0006020498,0.000405266,0.0003663298],"domain_scores_gemma":[0.9981811,0.000510369,0.0001891533,0.0002833582,0.0006664972,0.0001695075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004360295,0.00004425968,0.00003745598,0.00001268471,0.00001255358,0.000006594724,0.0002191021,0.00001001563,0.0006226372,0.6529816,0.00004478812,0.3459647],"study_design_scores_gemma":[0.0000807853,0.0002932858,0.0001162816,0.00010945,0.00000696549,0.00001322048,0.0001346913,0.1174789,0.007513685,0.8735382,0.0004155565,0.0002989994],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004118804,0.00001006524,0.9891099,0.005535972,0.0006685597,0.0002211678,0.0001927728,0.00008429998,0.003765368],"genre_scores_gemma":[0.9330574,0.0001282852,0.06599198,0.0003265624,0.0001414935,0.00007178215,0.00001356756,0.00001170822,0.0002572594],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9326455,"threshold_uncertainty_score":0.7082038,"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."}}