{"id":"W2172943111","doi":"10.1016/j.tcs.2013.09.026","title":"Accelerated training of max-margin Markov networks with kernels","year":2013,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; Google; University of Chicago; National Science Foundation","keywords":"Margin (machine learning); Computation; Markov chain; Convergence (economics); Projection (relational algebra); Computer science; Mathematical optimization; Euclidean geometry; Mathematics; Algorithm; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004989906,0.0002226099,0.0002822373,0.0001313363,0.0002852,0.000294063,0.002741618,0.00005089199,0.00008179841],"category_scores_gemma":[0.00003029356,0.0001592754,0.0000451687,0.002376079,0.002642799,0.001067773,0.000769686,0.0002461813,0.000041402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003259715,"about_ca_system_score_gemma":0.0000997311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003952908,"about_ca_topic_score_gemma":3.636648e-7,"domain_scores_codex":[0.9975718,0.0000752995,0.0003412216,0.0007459358,0.0005602746,0.0007054505],"domain_scores_gemma":[0.997811,0.0004213935,0.000141937,0.0009907308,0.0003305852,0.0003043117],"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":[0.000006971967,0.00003745718,0.00005144752,0.000003036476,0.000004822663,0.000003682746,0.0002970599,0.01348196,0.0006899945,0.8426237,0.00005968169,0.1427402],"study_design_scores_gemma":[0.000205738,0.0002178443,0.001976076,0.0000344079,0.000003388396,0.00003890156,0.00000818952,0.9376183,0.002066423,0.0575531,0.00004547713,0.0002321535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04021905,0.00002465862,0.9556038,0.0008302669,0.0001361978,0.0003897755,5.282099e-7,0.0002130054,0.002582701],"genre_scores_gemma":[0.6816356,0.00000457402,0.3179065,0.0003445585,0.00005838536,0.00003069673,6.055434e-7,0.000008808427,0.00001032076],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9241363,"threshold_uncertainty_score":0.9737502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01849413291799128,"score_gpt":0.2499440396265226,"score_spread":0.2314499067085313,"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."}}