We present a learning framework for Markovian decision processes that is based on optimization in the policy space. Instead of using relatively slow gradient-based optimization al...
We present a local learning rule in which Hebbian learning is conditional on an incorrect prediction of a reinforcement signal. We propose a biological interpretation of such a fr...
P. Read Montague, Peter Dayan, Steven J. Nowlan, T...
In this paper, we review five heuristic strategies for handling context-sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost...
This paper presents an extensible architectural model for general content-based analysis and indexing of video data which can be customised for a given problem domain. Video interp...
This paper explores an inferential system for recognizing visual patterns. The system is inspired by a recent memoryprediction theory and models the high-level architecture of the...