{"id":"W3205783011","doi":"","title":"Tighter Risk Certificates for Neural Networks","year":2020,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Laboratory; Army Research Office; Engineering and Physical Sciences Research Council; University College London; DeepMind; Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Artificial neural network; Artificial intelligence; Machine learning","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":[0.01314528,0.002404242,0.00319872,0.002235624,0.001362801,0.004098465,0.003376439,0.003499467,0.01164057],"category_scores_gemma":[0.08005717,0.001138206,0.001326911,0.001786998,0.005463227,0.01051394,0.008566078,0.01156343,0.002075659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003854887,"about_ca_system_score_gemma":0.002191681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001954016,"about_ca_topic_score_gemma":0.001693599,"domain_scores_codex":[0.9928256,0.002966239,0.0003419551,0.001353153,0.001919651,0.0005934085],"domain_scores_gemma":[0.9460365,0.04082591,0.002738187,0.004912341,0.003339501,0.002147474],"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":[0.000339511,0.0001197987,0.0008177698,0.000409873,0.00008474824,0.0001536876,0.0001694101,0.1756715,0.001550192,0.7572836,0.01050502,0.05289502],"study_design_scores_gemma":[0.0000314071,0.00007993137,0.00019701,0.00005738575,0.00001612194,0.0000601949,0.00001554502,0.266694,0.000578395,0.7305595,0.001691341,0.00001903408],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03197509,0.002511737,0.9452406,0.003542529,0.0004916966,0.00009602329,0.0003178105,0.0008475027,0.01497716],"genre_scores_gemma":[0.7553669,0.00422561,0.1887247,0.00206134,0.001939965,0.0005771646,0.001174316,0.001108886,0.04482111],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01314528,"threshold_uncertainty_score":0.06951976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02164719154634548,"score_gpt":0.2178465939448264,"score_spread":0.1961994023984809,"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."}}