10 citations · 29 across the 7 of their papers we have counts for
5 papers · 1 filter
Gauge theory and twins paradox of disentangled representations
X. Dong, L. Zhou
Achieving disentangled representations of information is one of the key goals of deep network based machine learning system. Recently there are more discussions on this issue. In t…
Understanding over-parameterized deep networks by geometrization
Xiao Dong, Ling Zhou
A complete understanding of the widely used over-parameterized deep networks is a key step for AI. In this work we try to give a geometric picture of over-parameterized deep networ…
Geometrization of deep networks for the interpretability of deep learning systems
Xiao Dong, Ling Zhou
How to understand deep learning systems remains an open problem. In this paper we propose that the answer may lie in the geometrization of deep networks. Geometrization is a bridge…
Demystifying AlphaGo Zero as AlphaGo GAN
Xiao Dong, Jiasong Wu, Ling Zhou
The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a worldwide discussion of the future of our human society with a mixed mood of hope, anxiousness, excitement an…
How deep learning works --The geometry of deep learning
Xiao Dong, Jiasong Wu, Ling Zhou
Why and how that deep learning works well on different tasks remains a mystery from a theoretical perspective. In this paper we draw a geometric picture of the deep learning system…