{"id":"W4225986943","doi":"10.1145/3502726","title":"On the Robustness of Metric Learning: An Adversarial Perspective","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Robustness (evolution); Pairwise comparison; Computer science; Metric (unit); Adversarial system; Artificial intelligence; Machine learning","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","open_science"],"consensus_categories":[],"category_scores_codex":[0.0009261291,0.0002917069,0.0003291103,0.0004022509,0.001256691,0.000188141,0.007974978,0.00006977667,0.0004969681],"category_scores_gemma":[0.0008027835,0.0002458874,0.0001454602,0.001789494,0.0001436572,0.002013296,0.0008124592,0.001448514,0.00003293797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003022416,"about_ca_system_score_gemma":0.0003151656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005903402,"about_ca_topic_score_gemma":0.00010148,"domain_scores_codex":[0.9962942,0.00121517,0.0003509263,0.001114546,0.0006933879,0.0003317745],"domain_scores_gemma":[0.9924831,0.002639567,0.0002366613,0.00443688,0.000117608,0.00008614439],"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":[0.0002589897,0.001145714,0.00004432381,0.000005832664,0.0002114782,0.000008411846,0.003144259,0.949181,0.00007939462,0.03019459,0.0005748522,0.01515115],"study_design_scores_gemma":[0.001968458,0.001285568,0.000665868,0.00004306127,0.0002233083,0.00001183923,0.01045502,0.9680746,0.0006083731,0.01115512,0.004719716,0.0007890845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01792742,0.0001342951,0.9765602,0.001121076,0.002161028,0.0003199448,0.0006358135,0.0001608647,0.0009793086],"genre_scores_gemma":[0.9923961,0.00001324233,0.006515086,0.00008091056,0.0001807444,0.00005504578,0.0002281117,0.00003779393,0.0004929465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9744687,"threshold_uncertainty_score":0.9999993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04692128029731266,"score_gpt":0.309908607807081,"score_spread":0.2629873275097683,"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."}}