Media formulation was improved using a combination of advanced machine learning techniques, thermodynamic modeling, and ...

Article citations More>> Snoek, J., Larochelle, H. and Adams, R.P. (2012) Practical Bayesian Optimization of Machine Learning Algorithms. arXiv: 1206.2944. has been cited by the following article: ...

Machine learning (ML) approaches have become ubiquitous in the search for new materials in recent years. Bayesian optimization (BO) based on Gaussian processes (GPs) has become a widely recognized ...

The present work proposes an improved Bayesian optimization (BO) method for the design of broadband radio frequency (RF) power amplifiers (PAs). A new acquisition function (AF) is incorporated into ...

This letter proposes a hierarchical multistate optimization (HMO) method for the microstrip reconfigurable bandpass filter (RBPF). HMO algorithm nests the inner global optimization algorithm within ...

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...

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 ...

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 ...