87 research outputs found
Encoding Robust Representation for Graph Generation
Generative networks have made it possible to generate meaningful signals such
as images and texts from simple noise. Recently, generative methods based on
GAN and VAE were developed for graphs and graph signals. However, the
mathematical properties of these methods are unclear, and training good
generative models is difficult. This work proposes a graph generation model
that uses a recent adaptation of Mallat's scattering transform to graphs. The
proposed model is naturally composed of an encoder and a decoder. The encoder
is a Gaussianized graph scattering transform, which is robust to signal and
graph manipulation. The decoder is a simple fully connected network that is
adapted to specific tasks, such as link prediction, signal generation on graphs
and full graph and signal generation. The training of our proposed system is
efficient since it is only applied to the decoder and the hardware requirements
are moderate. Numerical results demonstrate state-of-the-art performance of the
proposed system for both link prediction and graph and signal generation.Comment: 9 pages, 7 figures, 6 table
Functional Connectome of the Human Brain with Total Correlation
Recent studies proposed the use of Total Correlation to describe functional connectivity
among brain regions as a multivariate alternative to conventional pairwise measures such as correlation or mutual information. In this work, we build on this idea to infer a large-scale (whole-brain)
connectivity network based on Total Correlation and show the possibility of using this kind of
network as biomarkers of brain alterations. In particular, this work uses Correlation Explanation
(CorEx) to estimate Total Correlation. First, we prove that CorEx estimates of Total Correlation and
clustering results are trustable compared to ground truth values. Second, the inferred large-scale
connectivity network extracted from the more extensive open fMRI datasets is consistent with existing
neuroscience studies, but, interestingly, can estimate additional relations beyond pairwise regions.
And finally, we show how the connectivity graphs based on Total Correlation can also be an effective
tool to aid in the discovery of brain diseases
Multi-scale approaches for the statistical analysis of microarray data (with an application to 3D vesicle tracking)
The recent developments in experimental methods for gene data analysis, called microarrays, provide the possibility of interrogating changes in the expression of a vast number of genes in cell or tissue cultures and thus in depth exploration of disease conditions. As part of an ongoing program of research in Guy A. Rutter (G.A.R.) laboratory, Department of Biochemistry, University of Bristol, UK, with support from the Welcome Trust, we study the impact of established and of potentially new methods to the statistical analysis of gene expression data.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
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