One of the core problems of modern statistics is to approximate difficult-to-compute
probability densities. This problem is especially important in Bayesian statistics, which …

This tutorial presents a hands-on view of the field of multi-view stereo with a focus on
practical algorithms. Multi-view stereo algorithms are able to construct highly detailed 3D …

We present DiffCollage, a compositional diffusion model that can generate large content by
leveraging diffusion models trained on generating pieces of the large content. Our approach …

We introduce a new, efficient, principled and backpropagation-compatible algorithm for
learning a probability distribution on the weights of a neural network, called Bayes by …

Kernel classifiers and regressors designed for structured data, such as sequences, trees
and graphs, have significantly advanced a number of interdisciplinary areas such as …

Deep learning-based approaches have been developed to solve challenging problems in
wireless communications, leading to promising results. Early attempts adopted neural …

Inverse problems in statistical physics are motivated by the challenges of 'big data'in
different fields, in particular high-throughput experiments in biology. In inverse problems, the …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

Many real-world applications produce networked data such as the world-wide web
(hypertext documents connected via hyperlinks), social networks (for example, people …

Information theory and inference, often taught separately, are here united in one entertaining
textbook. These topics lie at the heart of many exciting areas of contemporary science and …