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 …

We provide the first global optimization landscape analysis of Neural Collapse--an intriguing
empirical phenomenon that arises in the last-layer classifiers and features of neural …

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 …

Optimization is a critical component in deep learning. We think optimization for neural
networks is an interesting topic for theoretical research due to various reasons. First, its …

This paper shows that every sublevel set of the loss function of a class of deep over-
parameterized neural nets with piecewise linear activation functions is connected and …

As science and engineering have become increasingly data-driven, the role of optimization
has expanded to touch almost every stage of the data analysis pipeline, from signal and …

This paper is aimed at applying deep artificial neural networks for solving system of ordinary
differential equations. We developed a vectorized algorithm and implemented using python …