Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identicaldistribution assumpt...
In many real-world classification problems the input contains a large number of potentially irrelevant features. This paper proposes a new Bayesian framework for determining the r...
Yuan (Alan) Qi, Thomas P. Minka, Rosalind W. Picar...
We study modular, automatic code generation from hierarchical block diagrams with synchronous semantics. Such diagrams are the fundamental model behind widespread tools in the emb...
Roberto Lublinerman, Christian Szegedy, Stavros Tr...
iStuff Mobile is the first rapid prototyping framework that helps explore new sensor-based interfaces with existing mobile phones. It focuses on sensor-enhanced physical interface...
Rafael Ballagas, Faraz Memon, Rene Reiners, Jan O....
— Local linearizations are ubiquitous in the control of robotic systems. Analytical methods, if available, can be used to obtain the linearization, but in complex robotics system...