Statistical Physics of Learning and Inference at ESANN 2019
Call for papers: Special Session
Statistical Physics of Learning and Inference at European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN2019)
Date: 24-26 April 2019, Bruges, Belgium
Deadline: November 26, 2018
This special session is meant to attract researchers who exploit analogies and concepts from statistical physics in the context of machine learning, inference, optimization, and related fields.
The exchange of ideas between statistical physics and computer science has been very fruitful and is currently gaining momentum again as a consequence of the revived interest in neural networks, machine learning and inference in general. Statistical physics methods complement other approaches to the theoretical understanding of machine learning processes and inference in stochasic modeling.
They facilitate, for instance, the study of dynamical and equilibrium properties of randomized training processes in model situations. At the same time, the approach inspires novel and efficient algorithms and facilitates interdisciplinary applications in a variety of scientific and technical disciplines. The tools and concepts applied in this context include information theory, the mathematical analysis of stochastic differential equations, methods borrowed from the statistical mechanics of disorder, mean field theory, variational calculus, renormalization group and other methods.
Potential topics include, but are not limited to:
- Probabilistic inference in, e.g., stochastic dynamical systems and complex networks
- Learning in Deep Networks and other architectures
- Complex optimization problems
- Emergent behavior in societies of agents
- Transient dynamics and equilibrium phenomena in machine learning
- The relation of statistical mechanics with information theory and mathematical statistics
- Applications, for instance in:
– systems biology and bioinformatics
– environmental modelling
– social systems
– signal processing
– complex optimization