45 citations · 273 across the 45 of their papers we have counts for
15 papers · 1 filter
G-invariant diffusion maps
Eitan Rosen, Xiuyuan Cheng, Yoel Shkolnisky
The diffusion maps embedding of data lying on a manifold has shown success in tasks such as dimensionality reduction, clustering, and data visualization. In this work, we consider…
The G-invariant graph Laplacian
Eitan Rosen, Paulina Hoyos, Xiuyuan Cheng +2
Graph Laplacian based algorithms for data lying on a manifold have been proven effective for tasks such as dimensionality reduction, clustering, and denoising. In this work, we con…
Spatio-temporal point processes with deep non-stationary kernels
Zheng Dong, Xiuyuan Cheng, Yao Xie
Point process data are becoming ubiquitous in modern applications, such as social networks, health care, and finance. Despite the powerful expressiveness of the popular recurrent n…
Neural network-based CUSUM for online change-point detection
Tingnan Gong, Junghwan Lee, Xiuyuan Cheng +1
Change-point detection, detecting an abrupt change in the data distribution from sequential data, is a fundamental problem in statistics and machine learning. CUSUM is a popular st…
Crime Hot-Spot Modeling via Topic Modeling and Relative Density Estimation
Jonathan Zhou, Sarah Huestis-Mitchell, Xiuyuan Cheng +1
We present a method to capture groupings of similar calls and determine their relative spatial distribution from a collection of crime record narratives. We first obtain a topic di…
Neural Spectral Marked Point Processes
Shixiang Zhu, Haoyun Wang, Zheng Dong +2
Self- and mutually-exciting point processes are popular models in machine learning and statistics for dependent discrete event data. To date, most existing models assume stationary…
Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples
Yixing Zhang, Xiuyuan Cheng, Galen Reeves
The Gaussian-smoothed optimal transport (GOT) framework, recently proposed by Goldfeld et al., scales to high dimensions in estimation and provides an alternative to entropy regula…
Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization
Zhongshu Xu, Yingzhou Li, Xiuyuan Cheng
Structured CNN designed using the prior information of problems potentially improves efficiency over conventional CNNs in various tasks in solving PDEs and inverse problems in sign…
A Dictionary Approach to Domain-Invariant Learning in Deep Networks
Ze Wang, Xiuyuan Cheng, Guillermo Sapiro +1
In this paper, we consider domain-invariant deep learning by explicitly modeling domain shifts with only a small amount of domain-specific parameters in a Convolutional Neural Netw…
Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters
Wei Zhu, Qiang Qiu, Robert Calderbank +2
Encoding the scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many computer vision tasks especially when deali…
Variational Diffusion Autoencoders with Random Walk Sampling
Henry Li, Ofir Lindenbaum, Xiuyuan Cheng +1
Variational autoencoders (VAEs) and generative adversarial networks (GANs) enjoy an intuitive connection to manifold learning: in training the decoder/generator is optimized to app…
A witness function based construction of discriminative models using Hermite polynomials
H. N. Mhaskar, A. Cloninger, X. Cheng
In machine learning, we are given a dataset of the form , drawn as i.i.d. samples from an unknown probability distribution ; the marginal distrib…
Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian
Xiuyuan Cheng, Gal Mishne
The extraction of clusters from a dataset which includes multiple clusters and a significant background component is a non-trivial task of practical importance. In image analysis t…
Deep Haar Scattering Networks
Xiuyuan Cheng, Xu Chen, Stephane Mallat
An orthogonal Haar scattering transform is a deep network, computed with a hierarchy of additions, subtractions and absolute values, over pairs of coefficients. It provides a simpl…
Unsupervised Deep Haar Scattering on Graphs
Xu Chen, Xiuyuan Cheng, Stéphane Mallat
The classification of high-dimensional data defined on graphs is particularly difficult when the graph geometry is unknown. We introduce a Haar scattering transform on graphs, whic…