Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processes

arXiv:2207.07654v3 Announce Type: replace-cross
Abstract: Uncertainty control and scalability to large datasets are the two main issues for the deployment of Gaussian process (GP) models within the autonomous machine learning-based prediction pipelines in material science and chemistry. One way to address both of these issues is by introducing the latent inducing point variables and choosing the right approximation for the marginal log-likelihood objective. Here, we empirically show that variational learning of the inducing points in a molecular descriptor space improves …

abstract arxiv autonomous chemistry control cs.lg data datasets deployment gaussian processes large datasets machine machine learning material physics.chem-ph physics.comp-ph pipelines prediction process processes scalability scalable science type uncertainty