There exist a number of reinforcement learning algorithms which learn by climbing the gradient of expected reward. Their long-run convergence has been proved, even in partially ob...
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is usin...
We propose a new approach for I/O scheduling that performs on-line simulation of the underlying disk. When simulation is integrated within a system, three key challenges must be a...
Florentina I. Popovici, Andrea C. Arpaci-Dusseau, ...
We present a method for specifying temporal constraints on trajectories of dynamical systems and enforcing them during qualitative simulation. This capability can be used to focus...
An automatic compound retrieval method is proposed to extract compounds within a text message. It uses n-gram mutual information, relative frequency count and parts of speech as t...