Variational methods are frequently used to approximate or bound the partition or likelihood function of a Markov random field. Methods based on mean field theory are guaranteed ...
Erik B. Sudderth, Martin J. Wainwright, Alan S. Wi...
Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods ...
This paper presents a data-driven approach for segmenting range data to enable a humanoid robot to perform interactive domestic tasks. Range data is segmented into geometric primi...
Many large -scale spatial data analysis problems involve an investigation of relationships in heterogeneous databases. In such situations, instead of making predictions uniformly a...
Aleksandar Lazarevic, Dragoljub Pokrajac, Zoran Ob...
We present a method for learning complex appearance mappings, such as occur with images of articulated objects. Traditional interpolation networks fail on this case since appearan...