Embedding images into a low dimensional space has a wide range of applications: visualization, clustering, and pre-processing for supervised learning. Traditional dimension reduct...
In this paper we present a scalable and distributed access structure for similarity search in metric spaces. The approach is based on the Content– addressable Network (CAN) parad...
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...