multi-objective optimization and applications of optimization in machine learning. Stanislaw H. Żak, PhD, is a professor in the Elmore Family School of Electrical and Computer Engineering at Purdue ...

In one, we study low-rank matrix optimization with ... property as long as the original objective function satisfies restricted strong convexity and smoothness properties. In two, we recognized that ...

A research team led by Prof. Wan Yinhua from the Institute of Process Engineering (IPE) of the Chinese Academy of Sciences ...

The KAIST team employed the multi-objective Bayesian optimization machine learning algorithm. This algorithm learned from simulated geometries to predict the best possible geometries for enhancing ...

Northwestern Engineering researchers have developed a new framework using machine learning that improves ... platform minimizes user intervention by employing multi-objective genetic algorithm ...

“Machine learning is normally very data intensive, and it’s difficult to generate a lot of data when you’re using high-quality data from finite element analysis. But the multi-objective Bayesian ...

multi-objective optimization, optimization for machine learning, deep learning and optimization, machine learning for optimization, optimization and learning under uncertainty, etc. More information ...

Ising machines can solve certain combinatorial optimization problems, but their efficiency could be improved with multi-spin flips ... tackling problems in machine learning, material design ...