The concept of a digital twin of Earth envisages the convergence of Big Earth Data with
physics-based models in an interactive computational framework that enables monitoring …

Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

Cardiovascular disease (CVD) is a general term for a series of heart or blood vessels
abnormality that serves as a global leading reason for death. The earlier the abnormal heart …

Conventional neural networks have been demonstrated to be a powerful framework for
background subtraction in video acquired by static cameras. Indeed, the well-known Self …

When and why can a neural network be successfully trained? This article provides an
overview of optimization algorithms and theory for training neural networks. First, we discuss …

Multilayered artificial neural networks are becoming a pervasive tool in a host of application
fields. At the heart of this deep learning revolution are familiar concepts from applied and …

Recent advances in deep learning for tomographic reconstructions have shown great
potential to create accurate and high quality images with a considerable speed up. In this …

Conditional generative adversarial networks (cGAN) have led to large improvements in the
task of conditional image generation, which lies at the heart of computer vision. The major …

We study the implicit regularization imposed by gradient descent for learning multi-layer
homogeneous functions including feed-forward fully connected and convolutional deep …

Recent applications of machine learning, in particular deep learning, motivate the need to
address the generalizability of the statistical inference approaches in physical sciences. In …