all AI news
COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence. (arXiv:2205.01224v1 [cs.LG])
Web: http://arxiv.org/abs/2205.01224
May 4, 2022, 1:11 a.m. | Andrew McDonald, Pang-Ning Tan, Lifeng Luo
cs.LG updates on arXiv.org arxiv.org
Normalizing flows, a popular class of deep generative models, often fail to
represent extreme phenomena observed in real-world processes. In particular,
existing normalizing flow architectures struggle to model multivariate
extremes, characterized by heavy-tailed marginal distributions and asymmetric
tail dependence among variables. In light of this shortcoming, we propose COMET
(COpula Multivariate ExTreme) Flows, which decompose the process of modeling a
joint distribution into two parts: (i) modeling its marginal distributions, and
(ii) modeling its copula distribution. COMET Flows capture heavy-tailed …
More from arxiv.org / cs.LG updates on arXiv.org
Latest AI/ML/Big Data Jobs
Director, Applied Mathematics & Computational Research Division
@ Lawrence Berkeley National Lab | Berkeley, Ca
Business Data Analyst
@ MainStreet Family Care | Birmingham, AL
Assistant/Associate Professor of the Practice in Business Analytics
@ Georgetown University McDonough School of Business | Washington DC
Senior Data Science Writer
@ NannyML | Remote
Director of AI/ML Engineering
@ Armis Industries | Remote (US only), St. Louis, California
Digital Analytics Manager
@ Patagonia | Ventura, California