151 citations · 632 across the 84 of their papers we have counts for
5 papers · 2 filters
Normalizing flow neural networks by JKO scheme
Chen Xu, Xiuyuan Cheng, Yao Xie
Normalizing flow is a class of deep generative models for efficient sampling and likelihood estimation, which achieves attractive performance, particularly in high dimensions. The…
Neural Stein critics with staged -regularization
Matthew Repasky, Xiuyuan Cheng, Yao Xie
Learning to differentiate model distributions from observed data is a fundamental problem in statistics and machine learning, and high-dimensional data remains a challenging settin…
Invertible Neural Networks for Graph Prediction
Chen Xu, Xiuyuan Cheng, Yao Xie
Graph prediction problems prevail in data analysis and machine learning. The inverse prediction problem, namely to infer input data from given output labels, is of emerging interes…
A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks
Jie Wang, Minshuo Chen, Tuo Zhao +2
Two-sample tests are important areas aiming to determine whether two collections of observations follow the same distribution or not. We propose two-sample tests based on integral…
An alternative approach to train neural networks using monotone variational inequality
Chen Xu, Xiuyuan Cheng, Yao Xie
We propose an alternative approach to neural network training using the monotone vector field, an idea inspired by the seminal work of Juditsky and Nemirovski [Juditsky & Nemirovsk…