Fanghui Liu

A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels

2019 · IEEE Transactions on Neural Networks and Learning Systems

Random Fourier features (RFFs) have been successfully employed to kernel approximation in large-scale situations. The rationale behind RFF relies on Bochner's theorem, but the condition is too strict and excludes many widely used kernels, e.g., …

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks

2024 · arXiv (Cornell University)

Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks as the curse of dimensionality (CoD) cannot be evaded when trying to approximate even …