540 citations · 841 across the 27 of their papers we have counts for
6 papers · 1 filter
Linear Stability Hypothesis and Rank Stratification for Nonlinear Models
Yaoyu Zhang, Zhongwang Zhang, Leyang Zhang +3
Models with nonlinear architectures/parameterizations such as deep neural networks (DNNs) are well known for their mysteriously good generalization performance at overparameterizat…
Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks
Zhiwei Bai, Tao Luo, Zhi-Qin John Xu +1
Understanding the relation between deep and shallow neural networks is extremely important for the theoretical study of deep learning. In this work, we discover an embedding princi…
An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation
Shuyu Yin, Tao Luo, Peilin Liu +1
Gradient descent or its variants are popular in training neural networks. However, in deep Q-learning with neural network approximation, a type of reinforcement learning, gradient…
Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width
Hanxu Zhou, Qixuan Zhou, Zhenyuan Jin +3
Substantial work indicates that the dynamics of neural networks (NNs) is closely related to their initialization of parameters. Inspired by the phase diagram for two-layer ReLU NNs…
Limitation of Characterizing Implicit Regularization by Data-independent Functions
Leyang Zhang, Zhi-Qin John Xu, Tao Luo +1
In recent years, understanding the implicit regularization of neural networks (NNs) has become a central task in deep learning theory. However, implicit regularization is itself no…
Overview frequency principle/spectral bias in deep learning
Zhi-Qin John Xu, Yaoyu Zhang, Tao Luo
Understanding deep learning is increasingly emergent as it penetrates more and more into industry and science. In recent years, a research line from Fourier analysis sheds lights o…