Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …

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 …

Neural network training relies on our ability to find" good" minimizers of highly non-convex
loss functions. It is well known that certain network architecture designs (eg, skip …

Many tasks in machine learning and signal processing can be solved by minimizing a
convex function of a measure. This includes sparse spikes deconvolution or training a …

We describe the new field of the mathematical analysis of deep learning. This field emerged
around a list of research questions that were not answered within the classical framework of …

While deep neural networks have overwhelmingly established state-of-the-art results in
many artificial intelligence problems, they can still be difficult to develop and debug. Recent …

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 …

Mapping road networks is currently both expensive and labor-intensive. High-resolution
aerial imagery provides a promising avenue to automatically infer a road network. Prior work …