Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Image classification and annotation are important problems
in computer vision, but rarely considered together. Intuitively,
annotations provide evidence for the class label,
and...
The ability to store and query uncertain information is of great benefit to databases that infer values from a set of observations, including databases of moving objects, sensor r...
We represent switching activity in VLSI circuits using a graphical probabilistic model based on Cascaded Bayesian Networks (CBN’s). We develop an elegant method for maintaining ...
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...