This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

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 provides a review and commentary on the past, present, and future of numerical
optimization algorithms in the context of machine learning applications. Through case …

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 …

The rapid growth of e-commerce has significantly increased the demand for advanced
techniques to address specific tasks in the e-commerce field. In this paper, we present a …

The accuracy of some density functional (DF) models widely used in material science
depends on empirical or free parameters that are commonly tuned using reference physical …

Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …

Motivated by recent increased interest in optimization algorithms for non-convex
optimization in application to training deep neural networks and other optimization problems …

In the past decade, deep learning became the prevalent methodology for predictive
modeling thanks to the remarkable accuracy of deep neural networks in tasks such as …

The enormous consumption of fossil fuels from various human activities leads to a significant
amount of anthropogenic CO 2 emission into the atmosphere, which has already massively …