{"id":"W2964610636","doi":"10.48550/arxiv.1907.11584","title":"Scalable Semi-Supervised SVM via Triply Stochastic Gradients","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Scalability; Computer science; Support vector machine; Classifier (UML); Kernel (algebra); Convexity; Artificial intelligence; Machine learning; Algorithm; Mathematics; Combinatorics; Database","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001932227,0.0003713057,0.0004032972,0.0003466144,0.0001745897,0.000159884,0.001882356,0.0003781351,0.0001259537],"category_scores_gemma":[0.00002655244,0.0004099268,0.0002567127,0.000564948,0.00006385475,0.0005913844,0.001908205,0.0006040361,0.001611132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001752304,"about_ca_system_score_gemma":0.0001687474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000137083,"about_ca_topic_score_gemma":0.000009683533,"domain_scores_codex":[0.9976778,0.0001363037,0.0002283423,0.001307086,0.0001634002,0.0004870593],"domain_scores_gemma":[0.9978728,0.00009761957,0.0002171702,0.001379131,0.0001896303,0.0002436714],"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":[0.0001723673,0.0006115922,0.002465656,0.0004123808,0.0002778375,0.0003837376,0.0008208216,0.9672538,0.001156879,0.01086515,0.009560895,0.006018867],"study_design_scores_gemma":[0.00104582,0.00007133452,0.0004453323,0.0002826593,0.00006284429,0.000005510588,0.00004564844,0.9753734,0.0003427819,0.02136618,0.0003696326,0.0005888938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2555027,0.00004597876,0.7400948,0.00007038349,0.001401523,0.0004129045,0.00001863121,0.0002617551,0.002191342],"genre_scores_gemma":[0.993486,0.0000555534,0.0009983968,0.0001935545,0.00006357057,0.000002281608,0.00005077656,0.00002239442,0.005127427],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7390964,"threshold_uncertainty_score":0.9998353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05085020534288383,"score_gpt":0.1806257253440142,"score_spread":0.1297755200011304,"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."}}