A summary is presented of the statistical mechanical theory of learning a rule with a neural
network, a rapidly advancing area which is closely related to other inverse problems …

This review covers original articles using deep learning in the biophotonic field published in
the last years. In these years deep learning, which is a subset of machine learning mostly …

Regularization is one of the crucial ingredients of deep learning, yet the term regularization
has various definitions, and regularization methods are often studied separately from each …

We propose an efficient method for approximating natural gradient descent in neural
networks which we call Kronecker-factored Approximate Curvature (K-FAC). K-FAC is based …

This chapter provides the background material and literature review for supervised
sequence labelling. Section 2.1 briefly reviews supervised learning in general. Section 2.2 …

Variational methods have been previously explored as a tractable approximation to
Bayesian inference for neural networks. However the approaches proposed so far have only …

Abstract Recurrent Neural Networks (RNNs) are powerful sequence models that were
believed to be difficult to train, and as a result they were rarely used in machine learning …

This paper presents a new hybrid approach for Global Solar Radiation (GSR) prediction
problems, based on deep learning approaches. Predictive models are useful ploys in solar …

This book provides the first comprehensive treatment of feed-forward neural networks from
the perspective of statistical pattern recognition. After introducing the basic concepts of …

The convergence of back-propagation learning is analyzed so as to explain common
phenomenon observedb y practitioners. Many undesirable behaviors of backprop can be …