10 citations · 29 across the 9 of their papers we have counts for
11 papers
Supervised Homogeneity Fusion: a Combinatorial Approach
Wen Wang, Shihao Wu, Ziwei Zhu +2
Fusing regression coefficients into homogenous groups can unveil those coefficients that share a common value within each group. Such groupwise homogeneity reduces the intrinsic di…
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…
Reducing Parameter Space for Neural Network Training
Tong Qin, Ling Zhou, Dongbin Xiu
For neural networks (NNs) with rectified linear unit (ReLU) or binary activation functions, we show that their training can be accomplished in a reduced parameter space. Specifical…
Spacetime as the optimal generative network of quantum states: a roadmap to QM=GR?
Xiao Dong, Ling Zhou
The idea that spacetime geometry is built from quantum entanglement has been widely accepted in the last years. But how exactly the geometry is related with quantum states is still…