6 citations · 6 across the 3 of their papers we have counts for
6 papers
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…
Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications
Ljubisa Stankovic, Danilo Mandic, Milos Dakovic +4
Many modern data analytics applications on graphs operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition,…
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…
Widely Linear Complex-valued Autoencoder: Dealing with Noncircularity in Generative-Discriminative Models
Zeyang Yu, Shengxi Li, Danilo Mandic
We propose a new structure for the complex-valued autoencoder by introducing additional degrees of freedom into its design through a widely linear (WL) transform. The corresponding…
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…
Watching Videos with Certain and Constant Quality: PID-based Quality Control Method
Yuhang Song, Mai Xu, Shengxi Li
In video coding, compressed videos with certain and constant quality can ensure quality of experience (QoE). To this end, we propose in this paper a novel PID-based quality control…