45 citations · 273 across the 45 of their papers we have counts for
17 papers · 1 filter
Neural Differential Recurrent Neural Network with Adaptive Time Steps
Yixuan Tan, Liyan Xie, Xiuyuan Cheng
The neural Ordinary Differential Equation (ODE) model has shown success in learning complex continuous-time processes from observations on discrete time stamps. In this work, we co…
Deep graph kernel point processes over networks
Zheng Dong, Matthew Repasky, Xiuyuan Cheng +1
Point process models are widely used for continuous-time discrete-event data, where each data point includes time and additional information called "marks," such as locations, node…
Computing high-dimensional optimal transport by flow neural networks
Chen Xu, Xiuyuan Cheng, Yao Xie
Computing optimal transport (OT) for general high-dimensional data has been a long-standing challenge. Despite much progress, most of the efforts including neural network methods h…
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…
SpecNet2: Orthogonalization-free spectral embedding by neural networks
Ziyu Chen, Yingzhou Li, Xiuyuan Cheng
Spectral methods which represent data points by eigenvectors of kernel matrices or graph Laplacian matrices have been a primary tool in unsupervised data analysis. In many applicat…
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…
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…
Neural Tangent Kernel Maximum Mean Discrepancy
Xiuyuan Cheng, Yao Xie
We present a novel neural network Maximum Mean Discrepancy (MMD) statistic by identifying a new connection between neural tangent kernel (NTK) and MMD. This connection enables us t…
Kernel Two-Sample Tests for Manifold Data
Xiuyuan Cheng, Yao Xie
We present a study of a kernel-based two-sample test statistic related to the Maximum Mean Discrepancy (MMD) in the manifold data setting, assuming that high-dimensional observatio…
Graph Convolution with Low-rank Learnable Local Filters
Xiuyuan Cheng, Zichen Miao, Qiang Qiu
Geometric variations like rotation, scaling, and viewpoint changes pose a significant challenge to visual understanding. One common solution is to directly model certain intrinsic…
Classification Logit Two-sample Testing by Neural Networks
Xiuyuan Cheng, Alexander Cloninger
The recent success of generative adversarial networks and variational learning suggests training a classifier network may work well in addressing the classical two-sample problem.…
Defending against Adversarial Images using Basis Functions Transformations
Uri Shaham, James Garritano, Yutaro Yamada +5
We study the effectiveness of various approaches that defend against adversarial attacks on deep networks via manipulations based on basis function representations of images. Speci…
DCFNet: Deep Neural Network with Decomposed Convolutional Filters
Qiang Qiu, Xiuyuan Cheng, Robert Calderbank +1
Filters in a Convolutional Neural Network (CNN) contain model parameters learned from enormous amounts of data. In this paper, we suggest to decompose convolutional filters in CNN…
Two-sample Statistics Based on Anisotropic Kernels
Xiuyuan Cheng, Alexander Cloninger, Ronald R. Coifman
The paper introduces a new kernel-based Maximum Mean Discrepancy (MMD) statistic for measuring the distance between two distributions given finitely-many multivariate samples. When…
Provable Estimation of the Number of Blocks in Block Models
Bowei Yan, Purnamrita Sarkar, Xiuyuan Cheng
Community detection is a fundamental unsupervised learning problem for unlabeled networks which has a broad range of applications. Many community detection algorithms assume that t…
A Deep Learning Approach to Unsupervised Ensemble Learning
Uri Shaham, Xiuyuan Cheng, Omer Dror +4
We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning. First, we prove that the popular model of Dawid and Skene, whic…