We investigate the use of self-predicting neural networks for autonomous robot learning within noisy or partially predictable environments. A benchmark experiment is performed in ...
James B. Marshall, Neil K. Makhija, Zachary D. Rot...
Abstract. This paper describes "cranking", a new committee formation algorithm. Cranking results in accurate and reliable committee predictions, even when applied to comp...
We present SpeedBoost, a natural extension of functional gradient descent, for learning anytime predictors, which automatically trade computation time for predictive accuracy by s...
— Device scaling and large integration increase the vulnerability of microprocessors to transient errors. One of the structures where errors can be most harmful is the register ï...
— Microarray data analysis is notoriously challenging as it involves a huge number of genes compared to only a limited number of samples. Gene selection, to detect the most signi...
Yi Shi, Zhipeng Cai, Lizhe Xu, Wei Ren, Randy Goeb...