Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...
Today, mobile smartphones are expected to be able to run the same complex, memory-intensive applications that were originally designed and coded for general-purpose processors. Ho...
We present new training methods that aim to mitigate local optima and slow convergence in unsupervised training by using additional imperfect objectives. In its simplest form, lat...
Valentin I. Spitkovsky, Hiyan Alshawi, Daniel Jura...
This paper describes a novel approach to the semantic relation detection problem. Instead of relying only on the training instances for a new relation, we leverage the knowledge l...
Chang Wang, James Fan, Aditya Kalyanpur, David Gon...
Abstract—Networks-on-Chip (NoCs) are implicitly fault tolerant due to their inherent redundancy. They can overcome defective cores, links and switches. As a side effect, yield is...
Atefe Dalirsani, Stefan Holst, Melanie Elm, Hans-J...