Recurrent neural networks serve as black-box models for nonlinear dynamical systems identification and time series prediction. Training of recurrent networks typically minimizes t...
We describe a framework for decomposing the distortion between two images into a linear combination of components. Unlike conventional linear bases such as those in Fourier or wav...
The responsiveness of networked applications is limited by communications delays, making network distance an important parameter in optimizing the choice of communications peers. S...
In spoken dialogue systems, Partially Observable Markov Decision Processes (POMDPs) provide a formal framework for making dialogue management decisions under uncertainty, but effi...
Compressing real-time input through bandwidth constrained connections has been studied within robotics, wireless sensor networks, and image processing. When there are bandwidth con...