Bert (HJ) Kappen

• Research interests
• Quantum machine learning
• Path integral control theory page.
• Publications
• A short biography
• Videolectures
• Google scholar citations

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• Master Projects

• Recent meetings organized
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The physics of machine learning

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. This is commonly referred to as intelligent behavior. Particular examples of intelligence 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 and novel computational methods for Bayesian inference and stochastic control theory using ideas and methods from statistical physics. These novel methods are used by me and by others in artificial intelligence research and computational models of brain function.

Bayesian Inference

• 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.

Control theory

Computational neuroscience

Quantum Machine Learning

Here is a recent paper: QBM Paper

Here is a recent talk:

Applications