The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
We propose a theoretical framework for specification and analysis of a class of learning problems that arise in open-ended environments that contain multiple, distributed, dynamic...
As learning agents move from research labs to the real world, it is increasingly important that human users, including those without programming skills, be able to teach agents de...
Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge in model fitting is...
Users of traditional tutorials and help systems often have difficulty finding the components described or pictured in the procedural instructions. Users also unintentionally miss ...