{"id":"W2137693597","doi":"10.1109/icip.2014.7025871","title":"Efficient Bayesian inference using fully connected conditional random fields with stochastic cliques","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Conditional random field; CRFS; Computer science; Inference; Computational complexity theory; Random graph; Theoretical computer science; Adjacency list; Clique; Bayesian network; Approximate inference; Algorithm; Graph; Artificial intelligence; Mathematics","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":[0.0033249,0.0007794467,0.001708892,0.001819984,0.0008615112,0.001238247,0.00243925,0.001637087,0.003104789],"category_scores_gemma":[0.01414769,0.001236972,0.001479793,0.00175692,0.001312433,0.002971726,0.001338262,0.002075985,0.0005238481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001628852,"about_ca_system_score_gemma":0.00236881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01609102,"about_ca_topic_score_gemma":0.01868969,"domain_scores_codex":[0.9984903,0.0007242674,0.00005937483,0.0003605804,0.0002603961,0.0001049676],"domain_scores_gemma":[0.989221,0.008953143,0.0004801114,0.0006712679,0.000508743,0.0001658375],"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":[0.0001300096,0.00005623228,0.0007940765,0.0001015904,0.00007012343,0.0001186948,0.00008085286,0.8849332,0.0008280913,0.05018063,0.002611292,0.06009521],"study_design_scores_gemma":[0.00001036156,0.000004458223,0.00006980534,0.000004442892,0.000004516939,0.00001163456,0.000003909074,0.9785688,0.0001309507,0.02093031,0.0002552592,0.000005593901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008636063,0.0001411516,0.9897983,0.0001790653,0.00001526989,0.00003164381,0.0001360725,0.0005094339,0.0005530099],"genre_scores_gemma":[0.4652287,0.0003888568,0.5293069,0.0002895066,0.0001537093,0.0002510427,0.001529708,0.0003089094,0.002542674],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01609102,"threshold_uncertainty_score":0.0319947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01411922932846265,"score_gpt":0.283885872463404,"score_spread":0.2697666431349413,"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."}}