Learning undirected graphical models such as Markov random fields is an important machine learning task with applications in many domains. Since it is usually intractable to learn...
Arthur Asuncion, Qiang Liu, Alexander T. Ihler, Pa...
Abstract--In this paper we analyze the energy-efficiency of distributed compression and its dependence on the node deployment strategies for Wireless Sensor Networks (WSNs). Reduce...
This paper generalizes Markov Random Field (MRF) stereo methods to the generation of surface relief (height) fields rather than disparity or depth maps. This generalization enable...
George Vogiatzis, Philip H. S. Torr, Steven M. Sei...
We study graphical modeling in the case of stringvalued random variables. Whereas a weighted finite-state transducer can model the probabilistic relationship between two strings, ...
The highest fidelity representations of realistic real-world materials currently used comprise Bidirectional Texture Functions (BTF). The BTF is a six dimensional function dependi...