Statistical Physics of Learning and Inference
Michael L. Biehl, Nestor Caticha, Manfred Opper, Thomas Villmann · University of Groningen research database (University of Groningen / Centre for Information Technology) · 2019
The exchange of ideas between statistical physics and computer science has been very fruitful and is currently gaining momentum 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 stochastic 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.