NETADIS Workshop on Modelling and Inference for Dynamics on Complex Interaction Networks: Joining Up Machine Learning and Statistical Physics, Montréal 2015 - VideoLectures - VideoLectures.NET

It is the goal of the proposed workshop to bring together researchers from the fields of machine learning and statistical physics in order to discuss the new challenges originating from dynamical data. Such data are modeled using a variety of approaches such as dynamic belief networks, continuous time analogues of these – as often used for disordered spin systems in statistical physics –, coupled stochastic differential equations for continuous random variables etc. The workshop provides a forum for exploring possible synergies between the inference and learning approaches developed for the various models. The experience from joint advances in the equilibrium domain suggests that there is much unexplored scope for progress on dynamical data.