Real-world networks often need to be designed under uncertainty, with only partial information and predictions of demand available at the outset of the design process. The field ...
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true d...
A new algorithm for training Restricted Boltzmann Machines is introduced. The algorithm, named Persistent Contrastive Divergence, is different from the standard Contrastive Diverg...
Design for specific customer service plays a crucial role for the majority of the market in modern electronics. However, adaptability to an individual customer results in increasi...
— Loss networks provide a powerful tool for the analysis and design of many communication and networking systems. It is well known that a large number of loss networks have produ...