We present a novel approach for automatically discovering spatio-temporal patterns in complex dynamic scenes. Similarly to recent non-object centric methods, we use low level visu...
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties ...
The work presented in this paper explores a supervised method for learning a probabilistic model of a lexicon of VerbNet classes. We intend for the probabilistic model to provide ...
Predictive state representations (PSRs) have recently been proposed as an alternative to partially observable Markov decision processes (POMDPs) for representing the state of a dy...
Matthew Rosencrantz, Geoffrey J. Gordon, Sebastian...
Assisting users with To Do lists presents new challenges for intelligent user interfaces. This paper presents our approach and an implemented system, BEAM, to process To Do list e...