Deep learning revolutionized data science, and recently its popularity has grown
exponentially, as did the amount of papers employing deep networks. Vision tasks, such as …

The paper systematically studies the impact of a range of recent advances in convolution
neural network (CNN) architectures and learning methods on the object categorization …

This article introduces a machine for sampling approximate model-X knockoffs for arbitrary
and unspecified data distributions using deep generative models. The main idea is to …

In this paper, we propose a new automatic hyperparameter selection approach for
determining the optimal network configuration (network structure and hyperparameters) for …

A major progress in deep multilayer neural networks (DNNs) is the invention of various
unsupervised pretraining methods to initialize network parameters which lead to good …

(DL) We present a novel approach to rank Deep Learning hyper-parameters through the
application of Sensitivity Analysis (SA). DL hyper-parameter tuning is crucial to model …

As an essential component for the usage of neural network, the structure of a neural network
(eg the number of hidden layer and the number of units in each layer) plays an important …

Multiresolution analysis and matrix factorization are foundational tools in computer vision. In
this work, we study the interface between these two distinct topics and obtain techniques to …

The necessity of depth has led to a family of designs referred to as very deep networks (eg,
GoogLeNet has 22 layers). As the depth increases even further, the need for appropriate …

The authors prove the gradient convergence of the deep learning-based numerical method
for high dimensional parabolic partial differential equations and backward stochastic …