We present an overview of FAB-MAP, an algorithm for place recognition and mapping developed for infrastructure-free mobile robot navigation in large environments. The system allow...
We study online learning when individual instances are corrupted by adversarially chosen random noise. We assume the noise distribution is unknown, and may change over time with n...
We present a novel approach to semisupervised learning which is based on statistical physics. Most of the former work in the field of semi-supervised learning classifies the point...
Traditionally, universities focus primarily on instructionist teaching. Such an understanding has been criticized from theoretical and practical points of view. We believe that so...
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...