This paper presents our Recurrent Control Neural Network (RCNN), which is a model-based approach for a data-efficient modelling and control of reinforcement learning problems in di...
The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network s...
This paper presents an evolutionary artificial neural network approach based on the pareto differential evolution algorithm augmented with local search for the prediction of breas...
Over the past few years, a number of approximate inference algorithms for networked data have been put forth. We empirically compare the performance of three of the popular algori...
— This work addresses the problem of selecting a subset of basis functions for a model linear in the parameters for regression tasks. Basis functions from a set of candidates are...