Variational-Bayes

Natural AI is Artificial Intelligence based on physics principles. The Free Energy Principle allows for designing agents that learn in real-time, are low-power and are explainable. They are powered by Bayesian mechanics, updating beliefs over states of the world and actions as data streams in. At BIASlab and LazyDynamics, we have developed a toolbox for scalable automatic Bayesian inference called RxInfer.

In the Bayesian approach to modelling, noise is not considered a nuisance but rather a reflection of uncertainty, expressed in terms of probability distributions. Variational Bayesian inference optimizes an approximation of the posterior distribution, allowing for a trade-off between accuracy and computational workload. In this talk, I will demonstrate how this technique may be applied in a simple system identification problem, compare it to classical techniques, and illustrate how modularity may be utilized to accelerate the model design cycle.

A talk for the Optimization / Machine Learning Competence group at Sioux Technologies. I presented the derivations to make a Bayesian filter for a standard Gaussian linear dynamical system, and outlined variational Bayesian inference as an extension. I briefly showed some of BIASlab’s research.

Message passing on factor graphs typically relies on a scheduling procedure, in which a central algorithm or compiler figures out which nodes should pass messages where at what time. This is not a biologically plausible mechanism. I explore the possibility of passing messages without a scheduler, where the nodes merely “react” to incoming messages.

MR images vary across medical centers due to calibration and acquisition protocols. But segmentations are relatively consistent. How …