Structural damage identification has received considerable attention during the past
decades. Although several reviews have been presented, some new developments have …

While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

We present MorphNet, an approach to automate the design of neural network structures.
MorphNet iteratively shrinks and expands a network, shrinking via a resource-weighted …

Machine learning is an interdisciplinary field of science and engineering that studies
mathematical theories and practical applications of systems that learn. This book introduces …

Accurate modeling of geographic distributions of species is crucial to various applications in
ecology and conservation. The best performing techniques often require some parameter …

The availability of detailed environmental data, together with inexpensive and powerful
computers, has fueled a rapid increase in predictive modeling of species environmental …

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 …

This paper introduces a general Bayesian framework for obtaining sparse solutions to
regression and classification tasks utilising models linear in the parameters. Although this …

We study the problem of modeling species geographic distributions, a critical problem in
conservation biology. We propose the use of maximum-entropy techniques for this problem …