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A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
We present a probabilistic framework for nonlinearities, based on doubly...
09/18/2017 ∙ by Qinliang Su, et al. ∙
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Unsupervised Learning with Truncated Gaussian Graphical Models
Gaussian graphical models (GGMs) are widely used for statistical modelin...
11/15/2016 ∙ by Qinliang Su, et al. ∙
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Nonlinear Statistical Learning with Truncated Gaussian Graphical Models
We introduce the truncated Gaussian graphical model (TGGM) as a novel fr...
06/02/2016 ∙ by Qinliang Su, et al. ∙
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Variational Gaussian Copula Inference
We utilize copulas to constitute a unified framework for constructing an...
06/19/2015 ∙ by Shaobo Han, et al. ∙
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Stick-Breaking Policy Learning in Dec-POMDPs
Expectation maximization (EM) has recently been shown to be an efficient...
05/01/2015 ∙ by Miao Liu, et al. ∙
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Cross-Domain Multitask Learning with Latent Probit Models
Learning multiple tasks across heterogeneous domains is a challenging pr...
06/27/2012 ∙ by Shaobo Han, et al. ∙
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