We introduce a novel framework for simultaneous structure and parameter learning in hidden-variable conditional probability models, based on an entropic prior and a solution for i...
This paper describes a class ofprobabilistic approximation algorithms based on bucket elimination which o er adjustable levels of accuracy ande ciency. We analyzethe approximation...
Background: The rapid growth of biomedical literature presents challenges for automatic text processing, and one of the challenges is abbreviation identification. The presence of ...
Sunghwan Sohn, Donald C. Comeau, Won Kim, W. John ...
Abstract. We present the discrete beeping communication model, which assumes nodes have minimal knowledge about their environment and severely limited communication capabilities. S...
We study in this paper two competing AIMD flows that share a common bottleneck link. When congestion occurs, one (or both) flows will suffer a loss that will cause its throughput ...
Eitan Altman, Rachid El Azouzi, David Ros, Bruno T...