{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001809188,0.0001780751,0.0001387365,0.0001614694,0.0004641381,0.0006744466,0.0007298024,0.00005923953,0.0008890122],"category_scores_gemma":[0.0002946565,0.0001521638,0.00004865457,0.0003552059,0.0001661871,0.0007612238,0.0001548712,0.0002963156,0.00007373423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005635167,"about_ca_system_score_gemma":0.0001480756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006273153,"about_ca_topic_score_gemma":0.00005391594,"domain_scores_codex":[0.9983064,0.0001777398,0.0002399962,0.0005173182,0.0005160583,0.0002424724],"domain_scores_gemma":[0.9983031,0.0002898319,0.0002061369,0.000330989,0.0007793015,0.00009065447],"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":[0.00008930377,0.0001044817,0.01422877,0.000002359583,0.0002359319,0.00002433666,0.002014135,0.29161,0.001742048,0.646888,0.001296645,0.04176399],"study_design_scores_gemma":[0.0003554166,0.0001630265,0.02760217,0.00004410026,0.000009162437,0.000009867151,0.0001195425,0.9642183,0.0005987037,0.004691644,0.001972152,0.0002159772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002713796,0.000005058921,0.9527256,0.003041938,0.0006490547,0.0001109814,0.00000387599,0.0001411027,0.04060853],"genre_scores_gemma":[0.9625701,0.00001125058,0.03511761,0.0002659703,0.0005221129,0.00004070061,0.00003563632,0.00001176982,0.001424793],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9598563,"threshold_uncertainty_score":0.973406,"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."}}