Accelerating Deep Learning with Shrinkage and Recall

Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in LASSO, we propose a shrinking Deep Learning with recall (sDLr) approach to speed up deep learning computation. We experiment shrinking Deep Learning with recall (sDLr) using Deep Neural Network (DNN), Deep Belief Network (DBN) and Convolution Neural Network (CNN) on 4 data sets. Results show that the speedup using shrinking Deep Learning with recall (sDLr) can reach more than 2.0 while still giving competitive classification performance.Comment: The 22nd IEEE International Conference on Parallel and Distributed Systems (ICPADS 2016

Holistic engineering design : a combined synchronous and asynchronous approach

To aid the creation and through-life support of large, complex engineering products, organizations are placing a greater emphasis on constructing complete and accurate records of design activities. Current documentary approaches are not sufficient to capture activities and decisions in their entirety and can lead to organizations revisiting and in some cases reworking design decisions in order to understand previous design episodes. Design activities are undertaken in a variety of modes; many of which are dichotomous, and thus each require separate documentary mechanisms to capture information in an efficient manner. It is possible to identify the modes of learning and transaction to describe whether an activity is aimed at increasing a level of understanding or whether it involves manipulating information to achieve a tangible task. The dichotomy of interest in this paper is that of synchronous and asynchronous working, where engineers may work alternately as part of a group or as individuals and where different forms of record are necessary to adequately capture the processes and rationale employed in each mode. This paper introduces complimentary approaches to achieving richer representations of design activities performed synchronously and asynchronously, and through the undertaking of a design based case study, highlights the benefit of each approach. The resulting records serve to provide a more complete depiction of activities undertaken, and provide positive direction for future co-development of the approaches

Gene selection algorithm by combining reliefF and mRMR

Background: Gene expression data usually contains a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminate biological samples of different types. In this paper, we present a two-stage selection algorithm by combining ReliefF and mRMR: In the first stage, ReliefF is applied to find a candidate gene set; In the second stage, mRMR method is applied to directly and explicitly reduce redundancy for selecting a compact yet effective gene subset from the candidate set. Results: We perform comprehensive experiments to compare the mRMR-ReliefF selection algorithm with ReliefF, mRMR and other feature selection methods using two classifiers as SVM and Naive Bayes, on seven different datasets. And we also provide all source codes and datasets for sharing with others. Conclusion: The experimental results show that the mRMR-ReliefF gene selection algorithm is very effective