We establish several approximate max-integral-flow / minmulticut theorems. While in general this ratio can be very large, we prove strong approximation ratios in the case where th...
We present a GPU-based algorithm for computing discretized distance functions on road networks. As applications, we provide algorithms for computing discrete Order-k Network Voron...
This paper describes a class ofprobabilistic approximation algorithms based on bucket elimination which o er adjustable levels of accuracy ande ciency. We analyzethe approximation...
Recently, there has been an increased focus on modeling uncertainty by distributions. Suppose we wish to compute a function of a stream whose elements are samples drawn independen...
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...