{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008724856,0.002867329,0.001839927,0.002522672,0.001017471,0.002540281,0.002394927,0.003051115,0.001505144],"category_scores_gemma":[0.06184284,0.0008873204,0.001885651,0.00169526,0.005880516,0.007431112,0.005489551,0.006412732,0.0004715812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002261467,"about_ca_system_score_gemma":0.00121863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001732851,"about_ca_topic_score_gemma":0.0007233074,"domain_scores_codex":[0.9910679,0.003880535,0.0005443894,0.001660833,0.002289719,0.0005565733],"domain_scores_gemma":[0.9481629,0.03964544,0.00364277,0.005218845,0.002455746,0.000874379],"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":[0.0001659623,0.00006000792,0.002101506,0.0002023731,0.0001911554,0.0001921633,0.0001684388,0.8275742,0.00338991,0.12822,0.001254928,0.03647931],"study_design_scores_gemma":[0.000007899924,0.0001277084,0.00031284,0.00004748107,0.00002444312,0.0001214788,0.00002629316,0.9165372,0.001944743,0.07993735,0.0008845998,0.00002796576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01619739,0.001809816,0.9777396,0.001089835,0.00009837188,0.00004830044,0.00007548917,0.0002429527,0.00269819],"genre_scores_gemma":[0.8461897,0.003681102,0.145357,0.0007511621,0.0005203259,0.0002490075,0.0003793454,0.0003290675,0.002543369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008724856,"threshold_uncertainty_score":0.04614198,"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."}}