“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 ...

To design the new nanostructures, the KAIST team employed a multi-objective Bayesian optimization (MBO) machine learning algorithm that learns from simulated shapes to predict optimal nanoscale ...

“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 ...

But the multi-objective Bayesian optimization algorithm only needed 400 data points, whereas other algorithms might need 20,000 or more.

Haojie Zhu, Ziyou Song, Weichao Zhuang, Heath Hofmann, Shuo Feng, Multi-objective Optimization for Connected and Automated Vehicles Using Machine Learning and Model Predictive Control, SAE ...

In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization and applications of optimization in machine learning. About the Purdue ...

But the multi-objective Bayesian optimization algorithm only needed 400 data points, whereas other algorithms might need 20,000 or more. So, we were able to work with a much smaller but an ...