Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a
suitably chosen reward function. However, designing such a reward function often requires …

This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that
aims to learn an interpretable representation of images, disentangled with respect to three …

Recent work on Bayesian optimization has shown its effectiveness in global optimization of
difficult black-box objective functions. Many real-world optimization problems of interest also …

Fundamental properties of light unavoidably impose features on images collected using
fluorescence microscopes. Accounting for these features is often critical in quantitatively …

One of the fundamental problems in Bayesian statistics is the approximation of the posterior
distribution. Gibbs sampler and coordinate ascent variational inference are renownedly …

Bayesian inference was once a gold standard for learning with neural networks, providing
accurate full predictive distributions and well calibrated uncertainty. However, scaling …

This work brings together two powerful concepts in Gaussian processes: the variational
approach to sparse approximation and the spectral representation of Gaussian processes …

Networks play a central role in modern data analysis, enabling us to reason about systems
by studying the relationships between their parts. Most often in network analysis, the edges …

Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The
rapid spread of the virus has created pandemic, and countries all over the world are …

No true extrasolar Earth analog is known. Hundreds of planets have been found around Sun-
like stars that are either Earth-sized but on shorter periods, or else on year-long orbits but …