{"id":"W3131298545","doi":"","title":"Differentially Private Generative Models Through Optimal Transport","year":2021,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Differential privacy; Computer science; Generative grammar; Generative model; Adversarial system; Artificial intelligence; Synthetic data; Machine learning; Data mining","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":[0.002445025,0.001008618,0.00101389,0.0005711591,0.0005401225,0.00123632,0.001634069,0.001450107,0.003186105],"category_scores_gemma":[0.007960571,0.0008052944,0.00128929,0.0006196293,0.002488642,0.002526418,0.004108474,0.003325932,0.0008258802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001849741,"about_ca_system_score_gemma":0.001300361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001694304,"about_ca_topic_score_gemma":0.002050374,"domain_scores_codex":[0.9987838,0.0004821594,0.00005051756,0.0002589877,0.0003068477,0.0001177697],"domain_scores_gemma":[0.9960787,0.00275197,0.0002406744,0.0006353553,0.0001760979,0.0001171873],"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":[0.0001250674,0.00003755492,0.0006333279,0.00005455019,0.00003317056,0.0001212502,0.0001261148,0.8498842,0.003144111,0.1108362,0.001390979,0.03361342],"study_design_scores_gemma":[0.00001040083,0.00001424154,0.00004187421,0.000007754472,0.000006151822,0.00003182989,0.000008276539,0.9433056,0.00150188,0.05437799,0.0006865953,0.000007438156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008198726,0.0001210612,0.9895699,0.000260894,0.00001729096,0.00002822943,0.00008879087,0.0004115205,0.001303447],"genre_scores_gemma":[0.7488615,0.0004934373,0.2372361,0.0006026246,0.0000710957,0.0002987793,0.0006869032,0.0006017769,0.01114788],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003186105,"threshold_uncertainty_score":0.01342088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02652168241920233,"score_gpt":0.2311368442439867,"score_spread":0.2046151618247844,"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."}}