{"id":"W2586119848","doi":"","title":"Blending learning and inference in conditional random fields","year":2016,"lang":"en","type":"article","venue":"Journal of Machine Learning Research","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Conditional random field; Conditional probability distribution; Approximate inference; Artificial intelligence; Computer science; Machine learning; Random variable; Structured prediction; Set (abstract data type); Mathematics; Conditional probability; Exponential family; Algorithm; Statistics","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":[0.004025817,0.00008444914,0.0002058064,0.0005868389,0.0001744241,0.0001149805,0.0004083619,0.00006142072,0.00005418157],"category_scores_gemma":[0.007135104,0.0000546816,0.0000454615,0.0004170458,0.0001008086,0.0008432222,0.0002879553,0.00145202,0.000006690723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006676032,"about_ca_system_score_gemma":0.00009794442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001366523,"about_ca_topic_score_gemma":0.00000338112,"domain_scores_codex":[0.997995,0.0006022983,0.0003304526,0.0001715598,0.0005994784,0.0003012074],"domain_scores_gemma":[0.9956856,0.003636696,0.0001662557,0.00009201479,0.000317175,0.000102303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003456359,0.00008318608,0.2621181,0.00004659869,0.00003009353,0.0005013337,0.0007754797,0.0005701208,0.02434456,0.01050566,0.0002172593,0.700462],"study_design_scores_gemma":[0.02929961,0.01259531,0.1315242,0.005412842,0.00002660523,0.002226527,0.0006694227,0.1097284,0.1145752,0.4068151,0.1852461,0.001880791],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06858215,0.000932098,0.9273162,0.002493783,0.00004122877,0.00006912098,3.350304e-7,0.00003398065,0.0005311274],"genre_scores_gemma":[0.9888924,0.001623892,0.008705841,0.00002264177,0.00007299629,0.000002133199,2.708823e-7,0.000006782934,0.0006730899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9203102,"threshold_uncertainty_score":0.8541902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04452678542633241,"score_gpt":0.4150474173640054,"score_spread":0.3705206319376729,"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."}}