33 citations · 126 across the 24 of their papers we have counts for
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stat.ML2016
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…
cs.CL2016★ 6 cited
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
Zhe Gan, Chunyuan Li, Changyou Chen +3
Recurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs using back-propagation through time often suffers fro…
stat.ML2016★ 9 cited
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)…