We consider probabilistic inference in general hybrid networks, which include continuous and discrete variables in an arbitrary topology. We reexamine the question of variable dis...
This paper suggests a systematic, orderly, process-based approach to stating software quality objectives and knowing if and when they have been achieved. We suggest that quality i...
Abstract. New methods of data collection, in particular the wide range of sensors and sensor networks that are being constructed, with the ability to collect real-time data streams...
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...
We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse,...