The phenomenal success of our integrated circuits managed to obscure an awkward fact: they're not always the best way to solve problems. The features of modern computers—binary operations, separated ...

Probabilistic programming has emerged as a powerful paradigm that integrates uncertainty directly into computational models. By embedding probabilistic constructs into conventional programming ...

Mathematics of Operations Research, Vol. 18, No. 1 (Feb., 1993), pp. 71-97 (27 pages) This paper is concerned with the design and probabilistic analysis of algorithms for the maximum-flow problem and ...

In recent years, the Massively Parallel Computation (MPC) model has gained significant attention. However, most of distributed and parallel graph algorithms in the MPC model are designed for static ...

Parallel tempering is a generic Markov chain Monte Carlo sampling method which allows good mixing with multimodal target distributions, where conventional Metropolis-Hastings algorithms often fail.

NVIDIA’s CUDA is a general purpose parallel computing platform and programming model that accelerates deep learning and other compute-intensive apps by taking advantage of the parallel processing ...

Algorithms define the rules, conditions, and methodology that an AI will use when processing and analyzing data. Written by eWEEK content and product recommendations are editorially independent. We ...