We look at the Machine Learning challenges through the eyes of the probabilistic framework of Bayesian statistics. Transforming ML into a statistical inference problem, we can improve its predictive power.
We leverage the power of Deep Learning methodologies to deliver accurate time series forecasts with uncertainty.
We bring Machine Learning technology to the world of Dynamical Systems. Time-varying information content in systems requires continuous reconfiguration of neural network topologies.