{"id":"W2168304982","doi":"10.14288/1.0167069","title":"Linear and parallel learning of Markov random fields","year":2014,"lang":"en","type":"article","venue":"Open Collections","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Embarrassingly parallel; Markov chain; Computer science; Bounded function; Log-linear model; Markov process; Mathematics; Degree (music); Markov model; Algorithm; Random field; Theoretical computer science; Parallel algorithm; Linear model; Statistics; Machine learning","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":[0.004441591,0.001059036,0.001454055,0.0009616265,0.0006843347,0.001787093,0.002881067,0.001524029,0.003014332],"category_scores_gemma":[0.02047818,0.0008814831,0.001356247,0.0008845933,0.002261614,0.004556222,0.003103061,0.00287808,0.0007664308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001653529,"about_ca_system_score_gemma":0.001536435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002888122,"about_ca_topic_score_gemma":0.00277825,"domain_scores_codex":[0.9977363,0.0008825323,0.000109321,0.00064964,0.0004486737,0.0001735474],"domain_scores_gemma":[0.9906479,0.006802259,0.0005854169,0.001187657,0.0005909826,0.0001858326],"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":[0.0001173714,0.00005731828,0.001056349,0.0001046411,0.00006508877,0.00007746839,0.0001230367,0.743087,0.001196747,0.1591054,0.001281821,0.09372781],"study_design_scores_gemma":[0.000009012035,0.00001129828,0.00004196615,0.000005041044,0.00000355894,0.00001686534,0.000005769974,0.9324951,0.0003699673,0.06665058,0.0003851635,0.000005655978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003882498,0.00009197735,0.9950599,0.0001269739,0.00001952861,0.00002263324,0.00002516013,0.0002370206,0.0005342374],"genre_scores_gemma":[0.3023387,0.0003703981,0.6910719,0.0003639305,0.0001965675,0.0003178036,0.0003729754,0.0003711636,0.004596645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004441591,"threshold_uncertainty_score":0.02348965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01233286228464193,"score_gpt":0.2532988352024782,"score_spread":0.2409659729178363,"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."}}