{"id":"W1927271208","doi":"","title":"Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models","year":2013,"lang":"en","type":"article","venue":"Infoscience (Ecole Polytechnique Fédérale de Lausanne)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gaussian process; Inference; Gaussian; Bayesian inference; Computer science; Conjugate prior; Algorithm; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics; Bayesian probability; Applied mathematics; Bayes' theorem; Physics","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":[0.00331632,0.0009435617,0.001413175,0.001056644,0.0008623892,0.001658383,0.001922842,0.001554181,0.003268606],"category_scores_gemma":[0.01325301,0.0009369449,0.001046694,0.001058146,0.001370001,0.002249285,0.00271481,0.003241474,0.0007621152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001728649,"about_ca_system_score_gemma":0.002369977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007896406,"about_ca_topic_score_gemma":0.01085796,"domain_scores_codex":[0.99911,0.0004429874,0.00003734796,0.0001470888,0.000186201,0.00007633166],"domain_scores_gemma":[0.9957671,0.003274144,0.0002163119,0.0002717317,0.000342602,0.0001281283],"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":[0.0001151613,0.00008170611,0.00153067,0.0001348537,0.00007704378,0.0001344859,0.0001891186,0.7338387,0.001741265,0.1813184,0.004364534,0.07647401],"study_design_scores_gemma":[0.000006887043,0.000002597432,0.00003741141,0.000004904498,0.000002152661,0.000009898236,0.000006260691,0.9738393,0.0001986688,0.02553383,0.000354708,0.000003355452],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00611239,0.0001676297,0.9925729,0.000177325,0.00001799813,0.00001941208,0.00004702633,0.0001717429,0.0007136205],"genre_scores_gemma":[0.2501231,0.0003761103,0.7445859,0.0002351872,0.00007738725,0.0001715152,0.0005999261,0.0004136547,0.003417207],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007896406,"threshold_uncertainty_score":0.01753855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01512019804397603,"score_gpt":0.2499689953666492,"score_spread":0.2348487973226732,"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."}}