Visualization for Understanding and Developing Machine Learning

Machine learning is in various flavors being deployed in a wide range of application domains in science and engineering, including autonomous vehicles, robot navigation, interaction systems and even medicine. As with most large-scale data driven approaches it is in most practical cases, e.g. in deep learning architectures, hard to analyze and understand what is the underlying learned model, how accurate is it, and exactly how it is solved? In this project, we will develop interactive visualization methods making both the learning process and the efficacy of the solution transparent to developers and users. Using deep learning and computer vision tasks as the application domain, we take a holistic approach by investigating ways of visualizing aspects of the training data, network structures, and inference results jointly in the same framework.

Contact: Daniel Jönsson