4 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2024
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…
cs.CV2022★ 4 cited
Demystifying CNNs for Images by Matched Filters
Shengxi Li, Xinyi Zhao, Ljubisa Stankovic +1
The success of convolution neural networks (CNN) has been revolutionising the way we approach and use intelligent machines in the Big Data era. Despite success, CNNs have been cons…
stat.ML2021
Von Mises-Fisher Elliptical Distribution
Shengxi Li, Danilo Mandic
A large class of modern probabilistic learning systems assumes symmetric distributions, however, real-world data tend to obey skewed distributions and are thus not always adequatel…