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Bert (HJ) Kappen is professor of physics at the Department of Biophysics, Radboud University, Nijmegen

Together with Ton Coolen he forms the research group on Physics of machine learning and complex systems

He is director of the Dutch foundation for Neural networks

He is visiting professor at Gatsby computational neuroscience unit at UCL London

Together with Riccardo Zecchina he leads the European Ellis program on machine learning called "Quantum and physics based machine learning".

Address: Department of Biophysics, Huygens Gebouw, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands

Email: b.kappen@science.ru.nl

One of the marked differences between computers and animals is the ability of the latter to learn and flexibly adapt to changing situations. Whereas computers need to be programmed with provisions for all possible future circumstances, the brain adapts its 'program' when needed, striking a remarkable balance between flexibility to adapt on the one hand and persistence by re-using pre-learned facts and skills on the other. Examples of such intelligent behavior are pattern recognition, learning and memory, reasoning, planning and motor control.

Due to the essential roles that noise and uncertainty play in perception and learning, a useful way to model intelligence is to use probability models. In the mid 90s, the fields of analog and digital computing as separate approaches to model intelligence, have begun to merge using the idea of Bayesian inference: One can generalize the logic of digital computation to a probabilistic calculus, embodied in a so-called graphical model. Similarly, one can generalize dynamical systems to stochastic dynamical systems that allow for a probabilistic description in terms of a Markov process. The Bayesian paradigm has greatly helped to integrate different schools of thought in particular in the field of artificial intelligence and machine learning but also provides a computational paradigm for neuroscience.

My research is dedicated to the design of efficient computational methods for Bayesian inference and stochastic control theory using ideas and methods from statistical physics and quantum physics. The aim of this research is to advance artificial intelligence research and computational models of brain function.

- The efficient approximate inference methods allow the design of large artificial reasoning systems. Currently, we are designing a diagnostic decision support system for internal medicine consisting of thousands of diagnoses, that should help the doctor during the diagnostic process (in collaboration with Radboud academic hospital).
- Design of high-dimensional Bayesian data analysis methods. The motivation is that Bayesian integration of the posterior distribution improves the statistical power of these methods compared to the maximum likelihood approaches. Approximate inference is used to efficiently compute statistics in the posterior distribution. One example is the use of the mean field approximation for sparse L0 regression. Another example is Gaussian Process regression with Monte Carlo sampling. In this case we have shown for yeast data that this method significantly outperforms all other methods and is able to identify novel genetic causes. In addition, these methods are applied to analyse neuroscience data (EEG, fMRI, MEG) for instance to find connectivity between brain regions.

This article on the quantum Boltzmann machine proposes a method to learn a quantum model from quantum or classical data

This article argues why adiabatic quantum annealing is unlikely to yield speed-up

Aladin is a software tool for performing efficient linkage analysis of a small number of distantly-related individuals. It estimates multipoint IBD probabilities and parametric LOD scores. Contact Kees Albers for details caa at sanger dot ac dot uk.