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Approximation of RKHS Functionals by Neural Networks
Motivated by the abundance of functional data such as time series and images, there has been a growing interest in integrating such data into neural networks and learning maps from function spaces to R (i. e., functionals).
Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks
This paper studies the binary classification of unbounded data from ${\mathbb R}^d$ generated under Gaussian Mixture Models (GMMs) using deep ReLU neural networks.
Learning Ability of Interpolating Deep Convolutional Neural Networks
It is frequently observed that overparameterized neural networks generalize well.
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