Recent research in neural networks and machine learning suggests that using many more
parameters than strictly required by the initial complexity of a regression problem can result …

Recent research in neural networks and machine learning suggests that using many more
parameters than strictly required by the initial complexity of a regression problem can result …

The weighted linearization is a generalization of the first-order Taylor approximation where
the computation of the Jacobian matrices at the point of interest is replaced by the …

Satisfaction of the strict saddle property has become a standard assumption in non-convex
optimization, and it ensures that many first-order optimization algorithms will almost always …

Large-scale optimization and control problems appear in applications such as robotics,
machine learning, smart power grids, and sensor networks. These problems involve many …

The main goal of the grant was to construct new approaches and algorithms for learning
dynamical systems from data. The objective of this research was to develop and analyze …

A Complete Bibliography of Publications in the SIAM Journal on Mathematics of Data Science
Page 1 A Complete Bibliography of Publications in the SIAM Journal on Mathematics of Data …

The main goal of the grant was to construct new approaches and algorithms for learning
dynamical systems from data. The objective of this research was to develop and analyze …