Generalization Ability of Wide Neural Networks on
We perform a study on the generalization ability of the wide two-layer ReLU neural network
on $\mathbb {R} $. We first establish some spectral properties of the neural tangent kernel …
on $\mathbb {R} $. We first establish some spectral properties of the neural tangent kernel …
Optimal rate of kernel regression in large dimensions
We perform a study on kernel regression for large-dimensional data (where the sample size
$ n $ is polynomially depending on the dimension $ d $ of the samples, ie, $ n\asymp …
$ n $ is polynomially depending on the dimension $ d $ of the samples, ie, $ n\asymp …
A survey on statistical theory of deep learning: Approximation, training dynamics, and generative models
N Suh, G Cheng - arXiv preprint arXiv:2401.07187, 2024 - arxiv.org
In this article, we review the literature on statistical theories of neural networks from three
perspectives. In the first part, results on excess risks for neural networks are reviewed in the …
perspectives. In the first part, results on excess risks for neural networks are reviewed in the …
On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains
In this paper, we provide a strategy to determine the eigenvalue decay rate (EDR) of a large
class of kernel functions defined on a general domain rather than $\mathbb {S}^{d} $. This …
class of kernel functions defined on a general domain rather than $\mathbb {S}^{d} $. This …