The application of functional networks in the automotive industry is still very slowly adopted into their development processes. Reasons for this are manifold. A functional networ...
Many embedded systems are implemented with a set of alternative function variants to adapt the system to different applications or environments. This paper proposes a novel approa...
Combining multiple global models (e.g. back-propagation based neural networks) is an effective technique for improving classification accuracy by reducing a variance through manipu...
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Background: Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods a...