Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
The complexity of today’s embedded applications requires modern high-performance embedded System-on-Chip (SoC) platforms to be multiprocessor architectures. Advances in FPGA tec...
We present a novel boosting algorithm where temporal consistency is addressed in a short-term way. Although temporal correlation of observed data may be an important cue for classi...
Pedro Canotilho Ribeiro, Plinio Moreno, José...
This work deals with the application of kernel methods to structured relational settings such as semantic knowledge bases expressed in Description Logics. Our method integrates a n...
This paper considers the problem of Bayesian inference in dynamical models with time-varying dimension. These models have been studied in the context of multiple target tracking pr...