While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …

Kernel methods are a class of learning machines for the fast recognition of nonlinear
patterns in any data set. In this paper, the applications of kernel methods for feature …

Variational Bayesian neural networks (BNNs) perform variational inference over weights, but
it is difficult to specify meaningful priors and approximate posteriors in a high-dimensional …

The Linac coherent light source x-ray free-electron laser is a complex scientific apparatus
which changes configurations multiple times per day, necessitating fast tuning strategies to …

In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …

Neural networks have achieved remarkable performance across various problem domains,
but their widespread applicability is hindered by inherent limitations such as overconfidence …

We propose deep convolutional Gaussian processes, a deep Gaussian process architecture
with convolutional structure. The model is a principled Bayesian framework for detecting …

We describe the development of the Intelligent Towing Tank, an automated experimental
facility guided by active learning to conduct a sequence of vortex-induced vibration (VIV) …

Transfer learning is to use the knowledge obtained from the source domain to improve the
learning efficiency when the target domain has insufficient labeled data. For regression …

The breakdown performance is a critical metric for power device design. This paper explores
the feasibility of efficiently predicting the breakdown performance of silicon on insulator (SOI) …