Reasoning about the past is of fundamental importance in several applications in computer science and artificial intelligence, including reactive systems and planning. In this pa...
Novel reconfigurable computing architectures exploit the inherent parallelism available in many signalprocessing problems. These architectures often consist of networks of compute...
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...
As applications are developed, functional tests ensure they continue to function as expected. Nowadays, functional testing is mostly done manually, with human testers verifying a ...
We consider the problem of learning incoherent sparse and lowrank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the spa...