This paper describes an on-going effort to investigate problems and approaches for achieving Web-service-based, dynamic and collaborative e-learning. In this work, a Learning Cont...
Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob...
Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, ...
This paper extends the traditional binocular stereo problem into the spacetime domain, in which a pair of video streams is matched simultaneously instead of matching pairs of imag...
This paper has no novel learning or statistics: it is concerned with making a wide class of preexisting statistics and learning algorithms computationally tractable when faced wit...
In this article, we propose a new approach for querying and indexing a database of trees with specific applications to XML datasets. Our approach relies on representing both the q...