We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem under ÐÔ norm, based on Ôstable distributions. Our scheme improves the running...
Mayur Datar, Nicole Immorlica, Piotr Indyk, Vahab ...
Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a ...
Background: Multiple sequence alignment is fundamental. Exponential growth in computation time appears to be inevitable when an optimal alignment is required for many sequences. E...
Hierarchical Packet Fair Queueing (H-PFQ)algorithms have the potential to simultaneously support guaranteed realtime service, rate-adaptive best-eort, and controlled linksharing s...
For many probability distributions of interest, it is quite difficult to obtain samples efficiently. Often, Markov chains are employed to obtain approximately random samples fro...