ForneyLab is a novel Julia package that allows the user to specify a probabilistic model as an FFG and pose inference problems on this FFG. ForneyLab is especially potent when applied to time-series data, where it attains comparable performance to Stan and Edward in significantly less computation time.
We develop a fully probabilistic approach to pure-tone audiometry. By assuming a Gaussian process based response model for test tones, the hearing threshold estimation problem becomes one of Bayesian inference. This allows the use of information-theoretic criteria to select optimal test tones.
Gesture recognition enables a natural extension of the way we currently interact with devices. Commercially available gesture recognition systems are usually pre-trained. We propose a method that allows users to define their own gestures using only a few training examples.
The primary mission of the BIASlab team is to develop in-situ trainable Bayesian Intelligent Agents for applications to wearable technology.
In this project you will be developing an agent to infer the dynamical parameters of an electro-mechanical positioning system.
This position is aimed at developing intelligent autonomous agents that learn from interacting with their environmnent.
In this project you are challenged to develop a software demonstrator for an intelligent agent that learns how to read text.