Bayesian methods for neural networks

Bayesian techniques have been developed over many years in a range of different fields, but have only recently been applied to the problem of learning in neural networks. As well as providing a consistent framework for statistical pattern recognition, the Bayesian approach offers a number of practical advantages including a potential solution to the problem of over-fitting. This chapter aims to provide an introductory overview of the application of Bayesian methods to neural networks. It assumes the reader is familiar with standard feed-forward network models and how to train them using conventional techniques.

Nanostructured Cu/Nb multilayers subjected to helium ion-irradiation

Helium ion-irradiation experiments have been performed in single layer Cu films, Nb films and Cu/Nb multilayer films with layer thickness varying from 2.5 nm to 100 nm each layer. Peak helium concentration approaches a few atomic percent with 6-9 displacement-per-atom in Cu and Nb. He bubbles were observed in single layer Cu and Nb films, as well as in Cu 100 nm/Nb 100 nm multilayers with helium bubbles aligned along layer interfaces. Helium bubbles are not resolved via transmission electron microscopy in Cu 2.5 nm/Nb 2.5 nm multilayers. These studies indicate that layer interface may play an important role in annihilating ion-irradiation induced defects such as vacancies and interstitials and have implications in improving the radiation tolerance of metallic materials using nanostructured multilayers. © 2007 Elsevier B.V. All rights reserved.