{"id":"W3113905995","doi":"10.48550/arxiv.2012.14574","title":"A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Pairwise comparison; Differential privacy; Computer science; Generative grammar; Generative model; Generalization; Population; Artificial intelligence; Adversarial system; Machine learning; Data mining; Mathematics","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":[0.002356067,0.0006535508,0.0007319123,0.0004721439,0.000336921,0.0009957141,0.001785304,0.001098747,0.003033298],"category_scores_gemma":[0.007450961,0.0005515037,0.0008741407,0.0005777788,0.001374249,0.002030486,0.002326348,0.002350608,0.0004770711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001720958,"about_ca_system_score_gemma":0.0009837084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002600342,"about_ca_topic_score_gemma":0.003678392,"domain_scores_codex":[0.9987565,0.0005454775,0.00003985422,0.0003584324,0.000189903,0.0001098041],"domain_scores_gemma":[0.9969112,0.002085588,0.000220355,0.0005032987,0.0001658986,0.0001136945],"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":[0.0001320317,0.0000596701,0.001615936,0.0000521376,0.00005679671,0.00008707996,0.0001765749,0.9041466,0.001544731,0.05753067,0.00111693,0.03348093],"study_design_scores_gemma":[0.000005803527,0.00001192976,0.00009367239,0.00000469138,0.000005271945,0.00001627869,0.000007133621,0.9810018,0.0004842782,0.01791637,0.0004484094,0.000004427769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0148887,0.0001356977,0.982474,0.0003941418,0.00002869268,0.00004225011,0.0002342918,0.0002974018,0.001504796],"genre_scores_gemma":[0.8707563,0.0002127273,0.1201631,0.0003607181,0.00005091345,0.0002373303,0.0005624708,0.0001423894,0.007514169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003033298,"threshold_uncertainty_score":0.01248652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1188231346139835,"score_gpt":0.2306895993715363,"score_spread":0.1118664647575528,"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."}}