6 citations · 6 across the 3 of their papers we have counts for
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cs.LG2020
Reciprocal Adversarial Learning via Characteristic Functions
Shengxi Li, Zeyang Yu, Min Xiang +1
Generative adversarial nets (GANs) have become a preferred tool for tasks involving complicated distributions. To stabilise the training and reduce the mode collapse of GANs, one o…
cs.LG2019
Solving general elliptical mixture models through an approximate Wasserstein manifold
Shengxi Li, Zeyang Yu, Min Xiang +1
We address the estimation problem for general finite mixture models, with a particular focus on the elliptical mixture models (EMMs). Compared to the widely adopted Kullback-Leible…
cs.LG2018
A universal framework for learning the elliptical mixture model
Shengxi Li, Zeyang Yu, Danilo Mandic
Mixture modelling using elliptical distributions promises enhanced robustness, flexibility and stability over the widely employed Gaussian mixture model (GMM). However, existing st…