{"id":"W3166610402","doi":"10.48550/arxiv.2102.08431","title":"Complex Momentum for Optimization in Games","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Momentum (technical analysis); Differentiable function; Convergence (economics); Bilinear interpolation; Generative grammar; Generalization; Adversarial system; Computer science; Mathematical optimization; Perspective (graphical); Gradient descent; Applied mathematics; Mathematics; Algorithm; Artificial intelligence; Pure mathematics; Artificial neural network; Mathematical analysis; Economics","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":[0.0017887,0.001693882,0.0008261541,0.0007074913,0.0005457586,0.001824602,0.00109212,0.001529576,0.007005892],"category_scores_gemma":[0.008488124,0.0004986093,0.0007277136,0.000674917,0.002837392,0.003071442,0.002420809,0.003752159,0.001556888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001413741,"about_ca_system_score_gemma":0.001051598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001512548,"about_ca_topic_score_gemma":0.001587407,"domain_scores_codex":[0.999078,0.0003839526,0.00004451518,0.0001752447,0.0002348578,0.00008337821],"domain_scores_gemma":[0.9981572,0.001109429,0.0001457134,0.0002729631,0.0001900683,0.0001246277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005405736,0.00005018243,0.0003537051,0.000104839,0.00003853731,0.00009308076,0.00007746647,0.2761695,0.00185744,0.6868145,0.00427745,0.03010921],"study_design_scores_gemma":[0.00001273163,0.00002321552,0.0000647134,0.00001807154,0.000004256264,0.00002627632,0.000006705743,0.7540971,0.0005611658,0.2419821,0.00319302,0.00001062545],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003357818,0.0002101847,0.989687,0.0005790408,0.000102771,0.00003006784,0.00004494526,0.0002005222,0.005787565],"genre_scores_gemma":[0.549336,0.001261014,0.4247558,0.001222376,0.0004227762,0.0004936898,0.0002469053,0.0008144835,0.02144702],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007005892,"threshold_uncertainty_score":0.02343708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08543563773179376,"score_gpt":0.1957959397475408,"score_spread":0.110360302015747,"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."}}