Many algorithm visualizations have been created, but little is known about which features are most important to their success. We believe that pedagogically useful visualizations ...
Purvi Saraiya, Clifford A. Shaffer, D. Scott McCri...
This paper describes a method and system for integrating machine learning with planning and data visualization for the management of mobile sensors for Earth science investigation...
Robert A. Morris, Nikunj C. Oza, Leslie Keely, Eli...
Abstract. Subspace mapping methods aim at projecting high-dimensional data into a subspace where a specific objective function is optimized. Such dimension reduction allows the re...
Axel J. Soto, Marc Strickert, Gustavo E. Vazquez, ...
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Deep learning has been successfully applied to perform non-linear embedding. In this paper, we present supervised embedding techniques that use a deep network to collapse classes....
Martin Renqiang Min, Laurens van der Maaten, Zinen...