AI News, Nuit Blanche
- On Friday, June 8, 2018
- By Read More
Recent breakthroughs in large-scale approximate Bayesian inference for sparse continuous variable models allow nonlinear Bayesian experimental design (active learning) and compressed sensing to be applied to sampling optimization of magnetic resonance imaging.
Within the recently established interdisciplinary MMCI Cluster of Excellence, 20 independent research groups are working in areas with strong overlaps to core machine learning application areas.
The Probabilistic Machine Learning group focusses on theory and applications of approximate Bayesian inference, and its scalable reduction to standard methods of scientific computing (numerical mathematics, efficient algorithms, signal processing, parallel processing).
We closely collaborate with the Center for High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen (with a range of MR scanners dedicated to basic research), and have close ties to the Empirical Inference group (headed by Bernhard Schoelkopf) at the same institute, beyond close connections to top machine learning groups in the UK and US.
We are looking for highly motivated, research-oriented individuals with an excellent grasp of the mathematics underlying approximate Bayesian inference, or/and numerical optimization and mathematics, or/and image and signal processing.
A strong theoretical background in a field relevant to analysis of statistical methods, or/and keen interest and capabilities in large-scale scientific programming are required.
- On Saturday, December 7, 2019
AMoveE 2014: Paul Blackwell
This tutorial was presented by Paul Blackwell on 5 May 2014 as part of the Symposium on Animal Movement and the Environment, held at the North Carolina ...
Deep Learning: A Review
The Academic Research Summit, co-organized by Microsoft Research and the Association for Computing Machinery, is a forum to foster meaningful discussion ...
Geordie Williamson, Lecture III - 23 January 2015
Geordie Williamson (Max-Planck-Institute, Bonn) - Lecture III This mini-course will be an introduction to perverse sheaves, ..