We present a principled Bayesian framework for modeling partial memberships of data points to clusters. Unlike a standard mixture model which assumes that each data point belongs ...
Katherine A. Heller, Sinead Williamson, Zoubin Gha...
High-dimensional problems arising from robot motion planning, biology, data mining, and geographic information systems often require the computation of k nearest neighbor (knn) gr...
This paper presents an automated, online approach to anomaly detection in high-content screening assays for pharmaceutical research. Online detection of anomalies is attractive be...
Adam Goode, Rahul Sukthankar, Lily B. Mummert, Mei...
Generative model learning is one of the key problems in machine learning and computer vision. Currently the use of generative models is limited due to the difficulty in effective...
Large repositories of source code create new challenges and opportunities for statistical machine learning. Here we first develop Sourcerer, an infrastructure for the automated c...
Erik Linstead, Paul Rigor, Sushil Krishna Bajracha...