In the field of medicine it is of vital importance to accurately predict the presence of a disease (diagnostic prediction) or the future occurrence of a certain event (prognostic...
Recurrent neural networks fail to deal with prediction tasks which do not satisfy the causality assumption. We propose to exploit bi-causality to extend the Recurrent Cascade Corr...
We propose a modular reinforcement learning architecture for non-linear, nonstationary control tasks, which we call multiple model-based reinforcement learning (MMRL). The basic i...
In this paper, we analyze restrictions of traditional communication performance models affecting the accuracy of analytical prediction of the execution time of collective communic...
Alexey L. Lastovetsky, Vladimir Rychkov, Maureen O...
For difficult prediction problems, practitioners often segment the data into relatively homogenous groups and then build a model for each group. This two-step procedure usually res...