Recently, we have introduced a novel approach to dynamic programming and reinforcement learning that is based on maintaining explicit representations of stationary distributions i...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...
This paper presents the limit laws of discrepancies defined via exponential sums, and algorithms (with error bounds) to approximate the corresponding distribution functions. The re...
We consider distributed algorithms to optimize random access multihop wireless networks in the presence of fading. Since the associated optimization problem is neither convex nor ...
Abstract-- Fountain codes are designed so that all input symbols can be recovered from a slightly larger number of coded symbols, with high probability using an iterative decoder. ...
This paper investigates the use of phoneme class conditional probabilities as features (posterior features) for template-based ASR. Using 75 words and 600 words task-independent a...
Serena Soldo, Mathew Magimai-Doss, Joel Pinto, Her...