3 papers
stat.ML2026
Smoothness Adaptivity in Constant-Depth Neural Networks: Optimal Rates via Smooth Activations
Yuhao Liu, Zilin Wang, Lei Wu +1
Smooth activation functions are ubiquitous in modern deep learning, yet their theoretical advantages over non-smooth counterparts remain poorly understood. In this work, we study b…
cs.LG2025
Depth-induced NTK: Bridging Over-parameterized Neural Networks and Deep Neural Kernels
Yong-Ming Tian, Shuang Liang, Shao-Qun Zhang +1
While deep learning has achieved remarkable success across a wide range of applications, its theoretical understanding of representation learning remains limited. Deep neural kerne…
cs.LG2024
A Unified Kernel for Neural Network Learning
Shao-Qun Zhang, Zong-Yi Chen, Yong-Ming Tian +1
Past decades have witnessed a great interest in the distinction and connection between neural network learning and kernel learning. Recent advancements have made theoretical progre…