55 citations · 79 across the 27 of their papers we have counts for
9 papers · 1 filter
Reliable Neural Collapse Approximation for Open-World Test-Time Adaptation
Jia-Qi Lin, Yuangang Pan, Chang-Dong Wang +3
Test-Time Adaptation (TTA) methods aim to bridge the domain gap between the source and target domains. However, traditional TTA methods become ineffective when the label distributi…
Uniform Convergence of Deep Neural Networks with Lipschitz Continuous Activation Functions and Variable Widths
Yuesheng Xu, Haizhang Zhang
We consider deep neural networks with a Lipschitz continuous activation function and with weight matrices of variable widths. We establish a uniform convergence analysis framework…
Convergence Analysis of Deep Residual Networks
Wentao Huang, Haizhang Zhang
Various powerful deep neural network architectures have made great contribution to the exciting successes of deep learning in the past two decades. Among them, deep Residual Networ…
Convergence of Deep Neural Networks with General Activation Functions and Pooling
Wentao Huang, Yuesheng Xu, Haizhang Zhang
Deep neural networks, as a powerful system to represent high dimensional complex functions, play a key role in deep learning. Convergence of deep neural networks is a fundamental i…
Convergence of Deep Convolutional Neural Networks
Yuesheng Xu, Haizhang Zhang
Convergence of deep neural networks as the depth of the networks tends to infinity is fundamental in building the mathematical foundation for deep learning. In a previous study, we…
Convergence of Deep ReLU Networks
Yuesheng Xu, Haizhang Zhang
We explore convergence of deep neural networks with the popular ReLU activation function, as the depth of the networks tends to infinity. To this end, we introduce the notion of ac…