6 citations · 14 across the 6 of their papers we have counts for
5 papers · 1 filter
CF-GO-Net: A Universal Distribution Learner via Characteristic Function Networks with Graph Optimizers
Zeyang Yu, Shengxi Li, Danilo Mandic
Generative models aim to learn the distribution of datasets, such as images, so as to be able to generate samples that statistically resemble real data. However, learning the under…
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…
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…
Artificial Intelligence for Prosthetics - challenge solutions
Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty +47
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a giv…
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…