首页|Deep data density estimation through Donsker-Varadhan representation
Deep data density estimation through Donsker-Varadhan representation
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Springer Nature
Estimating the data density is one of the challenging problem topics in the deep learning society. In this paper, we present a simple yet effective methodology for estimating the data density using the Donsker-Varadhan variational lower bound on the KL divergence and the modeling based on the deep neural network. We demonstrate that the optimal critic function associated with the Donsker-Varadhan representation on the KL divergence between the data and the uniform distribution can estimate the data density. Also, we present the deep neural network-based modeling and its stochastic learning procedure. The experimental results and possible applications of the proposed method demonstrate that it is competitive with the previous methods for data density estimation and has a lot of possibilities for various applications.
Donsker-Varadhan representationData density estimationKL-divergenceProbabilistic modeling
Seonho Park、Panos M. Pardalos
展开 >
Department of Industrial and Systems Engineering, University of Florida, Gainesville 32611, FL, USA
NETL
NSTL
Springer Nature
