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Can overfitted deep neural networks in adversarial training generalize? -- An approximation viewpoint
Adversarial training is a widely used method to improve the robustness of deep neural networks (DNNs) over adversarial perturbations.
Nonlinear functional regression by functional deep neural network with kernel embedding
With the rapid development of deep learning in various fields of science and technology, such as speech recognition, image classification, and natural language processing, recently it is also widely applied in the functional data analysis (FDA) with some empirical success.
Learning Theory of Distribution Regression with Neural Networks
In contrast to the classical regression methods, the input variables of distribution regression are probability measures.
Distributed Gradient Descent for Functional Learning
In the information era, to face the big data challenges {that} stem from functional data analysis very recently, we propose a novel distributed gradient descent functional learning (DGDFL) algorithm to tackle functional data across numerous local machines (processors) in the framework of reproducing kernel Hilbert space.
Theory of Deep Convolutional Neural Networks III: Approximating Radial Functions
We consider a family of deep neural networks consisting of two groups of convolutional layers, a downsampling operator, and a fully connected layer.
