We introduce a new technique for proving kernelization lower bounds, called cross-composition. A classical problem L cross-composes into a parameterized problem Q if an instance o...
Hans L. Bodlaender, Bart M. P. Jansen, Stefan Krat...
We consider the problem of estimating the uncertainty in large-scale linear statistical inverse problems with high-dimensional parameter spaces within the framework of Bayesian inf...
H. P. Flath, Lucas C. Wilcox, Volkan Akcelik, Judi...
Subordinate-level categorization typically rests on establishing salient distinctions between part-level characteristics of objects, in contrast to basic-level categorization, whe...
Ryan Farrell, Om Oza, Ning Zhang, Vlad I. Morariu,...
In this paper, we model the pair-wise similarities of a set of documents as a weighted network with a single cutoff parameter. Such a network can be thought of an ensemble of unwe...
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...