Probabilistic modelling of text data in the bagof-words representation has been dominated by directed graphical models such as pLSI, LDA, NMF, and discrete PCA. Recently, state of...
It is well-known that inference in graphical models is hard in the worst case, but tractable for models with bounded treewidth. We ask whether treewidth is the only structural cri...
Documents, such as those seen on Wikipedia and Folksonomy, have tended to be assigned with multiple topics as a meta-data. Therefore, it is more and more important to analyze a re...
We introduce a new class of probabilistic latent variable model called the Implicit Mixture of Conditional Restricted Boltzmann Machines (imCRBM) for use in human pose tracking. K...
Graham Taylor, Leonid Sigal, David Fleet, Geoffrey...
We propose an online topic model for sequentially analyzing the time evolution of topics in document collections. Topics naturally evolve with multiple timescales. For example, so...