Down-up sampling of images is an essential process for spatial scalability of the video coding standard. We propose an efficient down-up sampling method in spatial domain using DC...
As the first stage for discovering association rules, frequent itemsets mining is an important challenging task for large databases. Sampling provides an efficient way to get appro...
Poisson Disk sampling patterns are of interest to the graphics community because their blue-noise properties are desirable in sampling patterns for rendering, illumination, and ot...
Collapsed Gibbs sampling is a frequently applied method to approximate intractable integrals in probabilistic generative models such as latent Dirichlet allocation. This sampling ...
Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...