@conference {15001, title = {Deep Learning for System Trace Restoration}, year = {Submitted}, author = {I Sucholutsky and A Narayan and M Schonlau and S Fischmeister} } @article {14995, title = {ConvART: Improving Adaptive Resonance Theory for Unsupervised Image Clustering}, journal = {Journal of Computational Vision and Image Systems}, volume = {4}, year = {Accepted}, month = {Forthcoming}, address = {4th Annual Conference on Vision and Intelligent Systems}, abstract = {While supervised learning techniques have become increasingly adept at separating images into different classes, these techniques require large amounts of labelled data which may not always be available. We propose a novel neuro-dynamic method for unsupervised image clustering by combining 2 biologically-motivated models: Adaptive Resonance Theory (ART) and Convolutional Neural Networks (CNN). ART networks are unsupervised clustering algorithms that have high stability in preserving learned information while quickly learning new information. Meanwhile, a major property of CNNs is their translation and distortion invariance, which has led to their success in the domain of vision problems. By embedding convolutional layers into an ART network, the useful properties of both networks can be leveraged to identify different clusters within unlabelled image datasets and classify images into these clusters. In exploratory experiments, we demonstrate that this method greatly increases the performance of unsupervised ART networks on a benchmark image dataset.}, author = {I Sucholutsky and M Schonlau} } @article {14994, title = {Text mining with n-gram variables}, journal = {Stata Journal}, volume = {17}, year = {2017}, pages = {866-881}, abstract = {Text mining is the process of turning free text into numerical variables and then analyzing them with statistical techniques. We introduce the command\ ngram, which implements the most common approach to text mining, the "bag of words". An\ n-gram is a contiguous sequence of words in a text. Broadly speaking,\ ngram\ creates hundreds or thousands of variables, each recording how often the corresponding\ n-gram occurs in a given text. This is more useful than it sounds. We illustrate\ ngram\ with the categorization of text answers from two open-ended questions.}, url = {https://www.stata-journal.com/article.html?article=st0502}, author = {M Schonlau and N Guenther and I Sucholutsky} }