This paper proposes novel hierarchical self-organizing associative memory architecture for machine learning. This memory architecture is characterized with sparse and local interco...
In this paper we describe a novel technique which implements a spatiotemporal model as a set of sub-models based on first order logic. These sub-models model different, typicall...
This paper addresses the problem of classifying observations when features are context-sensitive, specifically when the testing set involves a context that is different from the t...
Hidden Markov models are a powerful technique to model and classify temporal sequences, such as in speech and gesture recognition. However, defining these models is still an art: ...
Methods for discovering causal knowledge from observational data have been a persistent topic of AI research for several decades. Essentially all of this work focuses on knowledge...
Marc Maier, Brian Taylor, Huseyin Oktay, David Jen...