La Trobe University

About: La Trobe University is a education organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Population & Health care. The organization has 13370 authors who have published 41291 publications receiving 1138269 citations. The organization is also known as: LaTrobe University & LTU.

Laboratory selection experiments using Drosophila: what do they really tell us?

Abstract: Laboratory selection experiments using Drosophila, and other organisms, are widely used in experimental biology. In particular, such experiments on D. melanogaster life history and stress-related traits have been instrumental in developing the emerging field of experimental evolution. However, similar selection experiments often produce inconsistent correlated responses to selection. Unfortunately, selection experiments are vulnerable to artifacts that are difficult to control. In spite of these problems, selection experiments are a valuable research tool and can contribute to our understanding of evolution in natural populations.

Randomness in neural networks: an overview

Abstract: Neural networks, as powerful tools for data mining and knowledge engineering, can learn from data to build feature-based classifiers and nonlinear predictive models. Training neural networks involves the optimization of nonconvex objective functions, and usually, the learning process is costly and infeasible for applications associated with data streams. A possible, albeit counterintuitive, alternative is to randomly assign a subset of the networks' weights so that the resulting optimization task can be formulated as a linear least-squares problem. This methodology can be applied to both feedforward and recurrent networks, and similar techniques can be used to approximate kernel functions. Many experimental results indicate that such randomized models can reach sound performance compared to fully adaptable ones, with a number of favorable benefits, including 1 simplicity of implementation, 2 faster learning with less intervention from human beings, and 3 possibility of leveraging overall linear regression and classification algorithms e.g., i¾?1 norm minimization for obtaining sparse formulations. This class of neural networks attractive and valuable to the data mining community, particularly for handling large scale data mining in real-time. However, the literature in the field is extremely vast and fragmented, with many results being reintroduced multiple times under different names. This overview aims to provide a self-contained, uniform introduction to the different ways in which randomization can be applied to the design of neural networks and kernel functions. A clear exposition of the basic framework underlying all these approaches helps to clarify innovative lines of research, open problems, and most importantly, foster the exchanges of well-known results throughout different communities. WIREs Data Mining Knowl Discov 2017, 7:e1200. doi: 10.1002/widm.1200