{"id":"W3037083547","doi":"","title":"Accelerating Smooth Games by Manipulating Spectral Shapes.","year":2020,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Monotone polygon; Eigenvalues and eigenvectors; Acceleration; Regularization (linguistics); Mathematical optimization; Gradient descent; Mathematics; Computer science; Applied mathematics; Algorithm; Geometry; Artificial intelligence","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":[0.0001321172,0.0001514128,0.0001484801,0.00007183506,0.000150602,0.0006693015,0.0004803781,0.00004372486,0.0007692915],"category_scores_gemma":[0.0003146343,0.000148701,0.00003395782,0.0002000629,0.0000517995,0.0003228875,0.0001074747,0.0001530954,0.0001083205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000025505,"about_ca_system_score_gemma":0.00005309845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000312625,"about_ca_topic_score_gemma":0.00001735881,"domain_scores_codex":[0.9985749,0.00005010974,0.0004151387,0.0003965327,0.0003908951,0.0001724208],"domain_scores_gemma":[0.9991835,0.0001850645,0.0001459791,0.00008871946,0.0002565565,0.000140181],"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":[0.00001327438,0.00003407338,0.000101792,0.000003230744,0.00002197894,0.000005953895,0.0004328148,0.002694009,0.0007522607,0.9217367,0.0006557845,0.07354819],"study_design_scores_gemma":[0.00002706443,0.00008206126,0.000178509,0.000009619956,0.000004957547,0.000001806491,0.0002040185,0.948773,0.0007047731,0.04945248,0.0003957361,0.0001660161],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004363392,0.00001129694,0.9806642,0.01235665,0.0001406581,0.000067946,0.00009038863,0.00006876467,0.006163765],"genre_scores_gemma":[0.8770982,0.00007116824,0.1202884,0.002221159,0.0001285159,0.000005292065,0.00006033887,0.000007413372,0.0001195689],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.946079,"threshold_uncertainty_score":0.8423203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1591484155708708,"score_gpt":0.3326122698059081,"score_spread":0.1734638542350373,"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."}}