{"id":"W2963359059","doi":"","title":"Variational Message Passing with Structured Inference Networks.","year":2018,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Inference; Graphical model; Computer science; Message passing; Theoretical computer science; Approximate inference; Variable elimination; Probabilistic logic; Algorithm; Artificial intelligence; Programming language","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":[0.004250643,0.001726022,0.001483373,0.001318289,0.0006896806,0.001674892,0.003509962,0.002714617,0.005148088],"category_scores_gemma":[0.0153968,0.00146032,0.001729877,0.001289437,0.002271336,0.004002621,0.003211066,0.004590097,0.0009937203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002541747,"about_ca_system_score_gemma":0.002230251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005334593,"about_ca_topic_score_gemma":0.007839497,"domain_scores_codex":[0.9981281,0.0009805523,0.00008834814,0.0003571116,0.0003232917,0.0001225158],"domain_scores_gemma":[0.9938508,0.004799758,0.0003408215,0.0005307423,0.0002993856,0.0001784101],"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":[0.00007646004,0.00005317088,0.0004197734,0.000124672,0.0001168108,0.00007604489,0.0001125361,0.662225,0.0008071132,0.2901669,0.002557486,0.04326401],"study_design_scores_gemma":[0.000008385926,0.000008975977,0.00002055564,0.000007480252,0.00000814022,0.00001168443,0.000003462743,0.9048937,0.0002423279,0.09409431,0.00069602,0.000004951289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00137434,0.0001659044,0.9971784,0.0002142038,0.00003092516,0.00003343673,0.00006368224,0.0002279844,0.0007110005],"genre_scores_gemma":[0.2563048,0.0007288526,0.7331628,0.0006880799,0.0003291746,0.0005904001,0.000755826,0.000470913,0.006969178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005334593,"threshold_uncertainty_score":0.02247983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02879153737919618,"score_gpt":0.3079606261640736,"score_spread":0.2791690887848774,"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."}}