{"id":"W2952193948","doi":"10.1109/icpr48806.2021.9412010","title":"Meta Learning via Learned Loss","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Max Planck Research School for Advanced Methods in Process and Systems Engineering; York University; European Commission; International Max Planck Research School for Environmental, Cellular and Molecular Microbiology; National Science Foundation","keywords":"Computer science; Reinforcement learning; Machine learning; Artificial intelligence; Parametric statistics; Regularization (linguistics); Pipeline (software); Code (set theory); Set (abstract data type); Meta learning (computer science); Process (computing); Source code; Function (biology); Task (project management); Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005420768,0.00269876,0.001998638,0.00130057,0.0005781074,0.00256163,0.003942176,0.00301554,0.00450277],"category_scores_gemma":[0.01870031,0.001252131,0.001347811,0.0008851869,0.002413454,0.00449426,0.004834434,0.005230918,0.002022943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001869012,"about_ca_system_score_gemma":0.001502768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008769154,"about_ca_topic_score_gemma":0.001239931,"domain_scores_codex":[0.9972288,0.001240776,0.0001417515,0.0005572527,0.0005829264,0.000248462],"domain_scores_gemma":[0.9936892,0.003479094,0.0005074064,0.001582498,0.0005241871,0.0002176315],"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":[0.000171053,0.0001195806,0.001113616,0.0001472463,0.0001454298,0.00008944551,0.0000579786,0.8832222,0.00215581,0.03284377,0.003814369,0.07611933],"study_design_scores_gemma":[0.00001406379,0.00005071369,0.00005963305,0.00002570123,0.00001250787,0.00002644346,0.000006308403,0.9728032,0.00110249,0.02528581,0.0006049143,0.000008289772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009954039,0.0005150357,0.9845947,0.0004801888,0.00005625405,0.00007357946,0.0001297911,0.001663537,0.002532823],"genre_scores_gemma":[0.6452981,0.0007204275,0.3435682,0.0009548474,0.0002196074,0.0007461486,0.0009485102,0.001149057,0.006395152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005420768,"threshold_uncertainty_score":0.02866817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05842071702153865,"score_gpt":0.3002474851927516,"score_spread":0.2418267681712129,"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."}}