Clustering is an important technique for understanding and analysis of large multi-dimensional datasets in many scientific applications. Most of clustering research to date has be...
We present a new approximate inference algorithm for Deep Boltzmann Machines (DBM's), a generative model with many layers of hidden variables. The algorithm learns a separate...
The latent topic model plays an important role in the unsupervised learning from a corpus, which provides a probabilistic interpretation of the corpus in terms of the latent topic...
Abstract— This paper proposes a method for learning viewpoint detection models for object categories that facilitate sequential object category recognition and viewpoint planning...
An approach to gesture recognition is presented in which gestures are modelled probabilistically as sequences of visual events. These events are matched to visual input using prob...