463 citations · 579 across the 9 of their papers we have counts for
9 papers
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
Qinliang Su, Xuejun Liao, Lawrence Carin
We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions. By setting the truncation points appropriately, we are able to generate v…
Unsupervised Learning with Truncated Gaussian Graphical Models
Qinliang Su, Xuejun Liao, Chunyuan Li +2
Gaussian graphical models (GGMs) are widely used for statistical modeling, because of ease of inference and the ubiquitous use of the normal distribution in practical approximation…
Nonlinear Statistical Learning with Truncated Gaussian Graphical Models
Qinliang Su, Xuejun Liao, Changyou Chen +1
We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning. A TGGM is a Gaussian graphical model (GGM)…
Variational Gaussian Copula Inference
Shaobo Han, Xuejun Liao, David B. Dunson +1
We utilize copulas to constitute a unified framework for constructing and optimizing variational proposals in hierarchical Bayesian models. For models with continuous and non-Gauss…
Stick-Breaking Policy Learning in Dec-POMDPs
Miao Liu, Christopher Amato, Xuejun Liao +2
Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs). However,…
Low-Cost Compressive Sensing for Color Video and Depth
Xin Yuan, Patrick Llull, Xuejun Liao +4
A simple and inexpensive (low-power and low-bandwidth) modification is made to a conventional off-the-shelf color video camera, from which we recover {multiple} color frames for ea…
Coded aperture compressive temporal imaging
Patrick Llull, Xuejun Liao, Xin Yuan +5
We use mechanical translation of a coded aperture for code division multiple access compression of video. We present experimental results for reconstruction at 148 frames per coded…
Adaptive Temporal Compressive Sensing for Video
Xin Yuan, Jianbo Yang, Patrick Llull +4
This paper introduces the concept of adaptive temporal compressive sensing (CS) for video. We propose a CS algorithm to adapt the compression ratio based on the scene's temporal co…
Cross-Domain Multitask Learning with Latent Probit Models
Shaobo Han, Xuejun Liao, Lawrence Carin
Learning multiple tasks across heterogeneous domains is a challenging problem since the feature space may not be the same for different tasks. We assume the data in multiple tasks…