19 citations · 40 across the 5 of their papers we have counts for
5 papers
Disentangled Recurrent Wasserstein Autoencoder
Jun Han, Martin Renqiang Min, Ligong Han +2
Learning disentangled representations leads to interpretable models and facilitates data generation with style transfer, which has been extensively studied on static data such as i…
-order Tensor Products with Invertible Linear Transforms
Jun Han
This paper studies the issues about tensors. Three typical kinds of tensor decomposition are mentioned. Among these decompositions, the t-SVD is proposed in this decade. Different…
Scalable Approximate Inference and Some Applications
Jun Han
Approximate inference in probability models is a fundamental task in machine learning. Approximate inference provides powerful tools to Bayesian reasoning, decision making, and Bay…
Stein Variational Inference for Discrete Distributions
Jun Han, Fan Ding, Xianglong Liu +3
Gradient-based approximate inference methods, such as Stein variational gradient descent (SVGD), provide simple and general-purpose inference engines for differentiable continuous…
Deep Generative Video Compression
Jun Han, Salvator Lombardo, Christopher Schroers +1
The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy. Here…