Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
In this paper, we follow a new path to arrive at the idea of a COMA — a Cache Only Memory Architecture. We show how the evolution of another architecture (ADARC) leads quite nat...
cal maps provide a useful abstraction for robotic navigation and planning. Although stochastic mapscan theoreticallybe learned using the Baum-Welch algorithm,without strong prior ...
This paper presents a critical discussion of the various approaches that have been used in the evaluation of Natural Language systems. We conclude that previous approaches have ne...
This paper describes a new model for constructing distributed systems called the Remote Memory Model. The remote memory model consists of several client machines, one or more dedi...