Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the ...

In order to evaluate a dataset of over 11 million cells from a study of dengue fever, Yale researchers developed a cutting-edge neural network that recognizes and represents patterns in large datasets ...

Clustering methods provide a powerful tool for the exploratory analysis of high-dimension, low—sample size (HDLSS) data sets, such as gene expression microarray data. A fundamental statistical issue ...

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...

In this paper, we introduce a new method to analyse HIV using a combination of autoencoder networks and genetic algorithms. The proposed method is tested on a set of demographic properties of ...