We propose efficient particle smoothing methods for generalized state-spaces models. Particle smoothing is an expensive O(N2 ) algorithm, where N is the number of particles. We ov...
Mike Klaas, Mark Briers, Nando de Freitas, Arnaud ...
Abstract. In contrast to the standard inductive inference setting of predictive machine learning, in real world learning problems often the test instances are already available at ...
This article discusses a telecollaborative activity that combines many strategies of interest in teacher education (i.e., case-based learning, online discussion, cross-university ...
The aim of this work is to learn a shape prior model
for an object class and to improve shape matching with the
learned shape prior. Given images of example instances,
we can le...
In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing...
Olivier Chapelle, Pannagadatta K. Shivaswamy, Srin...