A review on generative Boltzmann networks applied to dynamic systems. (15th January 2021) - Souriau, Rémi; Lerbet, Jean; Chen, Hsin; Vigneron, Vincent
Graphical abstract: Abstract: The modelling of dynamic system is a challenging problem in a large number of applications like prediction, bio-data modelling, computer vision or time-series processing. To face the complexity and the non-linearity of data, new models are regularly proposed through the literature. Among proposed models artificial neural network (ANN) have benefit of a large interest in the scientist community. The use of latent variables to extract and diffuse complex features in multilayer feedforward neural networks provide usually excellent results. In 1982, Hopfield proposes a generative and deterministic neural network to model a physical system. His work leads to the emergence of a large number of generative neural networks: Boltzmann Machine and its extensions. Different applications lead researchers to propose new extensions for the Boltzmann machine to handle dynamic systems, continuous variables or systems with complex features. In parallel, a new model named the Diffusion Network has emerged, also inspired from Hopfield network but with continuous stochastic properties and designed to solve stochastic differential equations.