4 papers
Almost-lossless compression of a low-rank random tensor
Minh Thanh Vu
In this work, we establish an asymptotic limit of almost-lossless compression of a random, finite alphabet tensor which admits a low-rank canonical polyadic decomposition.
Belief Propagation for Approximate Inference
Dong Liu, Minh Thành Vu, Zuxing Li +1
Belief propagation (BP) algorithm is a widely used message-passing method for inference in graphical models. BP on loop-free graphs converges in linear time. But for graphs with lo…
Neural Network based Explicit Mixture Models and Expectation-maximization based Learning
Dong Liu, Minh Thành Vu, Saikat Chatterjee +1
We propose two neural network based mixture models in this article. The proposed mixture models are explicit in nature. The explicit models have analytical forms with the advantage…
Entropy-regularized Optimal Transport Generative Models
Dong Liu, Minh Thành Vu, Saikat Chatterjee +1
We investigate the use of entropy-regularized optimal transport (EOT) cost in developing generative models to learn implicit distributions. Two generative models are proposed. One…