{"id":"W3152676947","doi":"","title":"Dataset Inference: Ownership Resolution in Machine Learning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Adversary; Computer science; Inference; Artificial intelligence; Machine learning; Key (lock); Set (abstract data type); Process (computing); Focus (optics); Statistical model; Decision boundary; Computer security","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":[0.02128808,0.001386632,0.002168166,0.00228741,0.001850415,0.004142007,0.0073563,0.004207359,0.002705083],"category_scores_gemma":[0.08836602,0.001231679,0.002149719,0.002449413,0.00613975,0.01433927,0.01141232,0.008220665,0.001006145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002217591,"about_ca_system_score_gemma":0.00267821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002043197,"about_ca_topic_score_gemma":0.001566343,"domain_scores_codex":[0.9777853,0.01079635,0.001273449,0.004236441,0.005144686,0.0007638288],"domain_scores_gemma":[0.910605,0.04951808,0.004307653,0.03205317,0.002667084,0.0008488835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001215772,0.0003424033,0.02114332,0.0006834434,0.0005125957,0.0006778205,0.001274662,0.1869112,0.004226377,0.2906286,0.02438039,0.4680035],"study_design_scores_gemma":[0.0000673918,0.0000912654,0.0009671648,0.0001206321,0.00005239706,0.0002908721,0.0001269072,0.6527293,0.006197548,0.331944,0.007362157,0.00005033069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01696149,0.001226781,0.9712191,0.004099017,0.0001324916,0.0001277189,0.0005961463,0.003542974,0.002094331],"genre_scores_gemma":[0.6720545,0.0009896,0.3185834,0.002042216,0.0004831865,0.0004509815,0.002271111,0.0005181806,0.002606776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02128808,"threshold_uncertainty_score":0.1125835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09053814841093495,"score_gpt":0.2287708281663123,"score_spread":0.1382326797553774,"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."}}