Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
The widely used engineering decisions concerning the performance of technological equipment for process industries are usually deterministic. Since the early 1990s probabilistic m...
A statistical framework for modeling and prediction of binary matrices is presented. The method is applied to social network analysis, specifically the database of US Supreme Cou...
Eric Wang, Jorge Silva, Rebecca Willett, Lawrence ...
With the goal to generate more scalable algorithms with higher efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towards combining classical tec...
To have a robust and informative image content representation for image categorization, we often need to extract as many as possible visual features at various locations, scales a...