We address the issue of classifying complex data. We focus on three main sources of complexity, namely, the high dimensionality of the observed data, the dependencies between these...
In this paper, we present a model of distributed parameter estimation in networks, where agents have access to partially informative measurements over time. Each agent faces a loca...
This paper describes a computationally feasible approximation to the AIXI agent, a universal reinforcement learning agent for arbitrary environments. AIXI is scaled down in two ke...
Joel Veness, Kee Siong Ng, Marcus Hutter, William ...
Abstract. We show how the “Online Sparse Coding Neural Gas” algorithm can be applied to a more realistic model of the “Cocktail Party Problem”. We consider a setting where ...
The Web is a vast, dynamic source of information and resources. Because of its size and diversity, it is increasingly likely that if the information one seeks is not already there...