Both teaching and learning multithreaded ing are complex tasks, due to the abstraction of the concepts, the non-determinism of the scheduler, the impossibility of using classical s...
Giovanni Malnati, Caterina Maria Cuva, Claudia Bar...
We present SpeedBoost, a natural extension of functional gradient descent, for learning anytime predictors, which automatically trade computation time for predictive accuracy by s...
The use of motor primitives for the generation of complex movements is a relatively new and interesting idea for dimensionality reduction in robot control. We propose a framework ...
Although necessary, learning to discover new solutions is often long and difficult, even for supposedly simple tasks such as counting. On the other hand, learning by imitation pr...
Model-based Bayesian reinforcement learning has generated significant interest in the AI community as it provides an elegant solution to the optimal exploration-exploitation trade...