It has long been known that a single-layer fully-connected neural network with an iid prior
over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network …

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 composition of multiple Gaussian Processes as a Deep Gaussian Process DGP
enables a deep probabilistic nonparametric approach to flexibly tackle complex machine …

We introduce a scalable approach to Gaussian process inference that combines spatio-
temporal filtering with natural gradient variational inference, resulting in a non-conjugate GP …

Introduction Phenomics has emerged as important tool to bridge the genotype-phenotype
gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its …

We introduce the implicit processes (IPs), a stochastic process that places implicitly defined
multivariate distributions over any finite collections of random variables. IPs are therefore …

This research introduces a machine learning (ML)-based methodology for the optimal blank
design of components manufactured through the deep drawing process, considering the …

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

We consider evidence integration from potentially dependent observation processes under
varying spatio-temporal sampling resolutions and noise levels. We offer a multi-resolution …

Glucose monitoring technologies allow users to monitor glycemic fluctuations (eg, blood
glucose levels). This is particularly important for individuals who have diabetes mellitus …