Approximate Sampling using an Accelerated Metropolis-Hastings based on Bayesian Optimization and Gaussian Processes

no code implementations • 21 Oct 2019 • Asif J. Chowdhury, Gabriel Terejanu

The main innovations include the use of Bayesian optimization to reach the high probability region quickly, emulating the target distribution using Gaussian processes (GP), and using Laplace approximation of the GP to build a proposal distribution that captures the underlying correlation better.

Bayesian Optimization Gaussian Processes

A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications

no code implementations • 1 Oct 2019 • Asif J. Chowdhury, Wenqiang Yang, Kareem E. Abdelfatah, Mehdi Zare, Andreas Heyden, Gabriel Terejanu

Our investigations also included the case when the species data used to train the predictive model is of different size relative to the species the model tries to predict - an extrapolation in the data space which is typically difficult with regular machine learning models.

BIG-bench Machine Learning