This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan...
Sridhar Mahadevan, Sarah Osentoski, Jeffrey Johns,...
Artificial neural networks (ANNs) have been successfully applied to solve a variety of classification and function approximation problems. Although ANNs can generally predict bett...
Solving design and analysis problems in physical worlds requires the representatio n of large amounts of knowledge. Recently, there has been much interest in explicitly making ass...
Sanjaya Addanki, Roberto Cremonini, J. Scott Penbe...
We propose a new method for the blind separation of multiple binary signals from a single general nonlinear mixture. In addition to the usual independence assumption on the input ...
Konstantinos I. Diamantaras, Theophilos Papadimitr...
Statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden Markov trees (HMTs). However, in linear inverse problems such as d...
Nikhil S. Rao, Robert D. Nowak, Stephen J. Wright,...