Abstract Software-Defined Networking (SDN) architecture has emerged in response to
limitations of traditional networking architectures in satisfying today's complex networking …

Adiabatic quantum computing (AQC) started as an approach to solving optimization
problems and has evolved into an important universal alternative to the standard circuit …

Scaling up deep neural network capacity has been known as an effective approach to
improving model quality for several different machine learning tasks. In many cases …

Learn to understand and implement the latest machine learning innovations to improve your
investment performance Machine learning (ML) is changing virtually every aspect of our …

Faced with saturation of Moore's law and increasing size and dimension of data, system
designers have increasingly resorted to parallel and distributed computing to reduce …

This paper quantitatively characterizes the approximation power of deep feed-forward
neural networks (FNNs) in terms of the number of neurons. It is shown by construction that …

In the six years that have passed since the publication of the first edition of this book,
iterative methods for linear systems have made good progress in scientific and engineering …

Problems with multiple objectives arise in a natural fashion in most disciplines and their
solution has been a challenge to researchers for a long time. Despite the considerable …

Scientists working with large volumes of high-dimensional data, such as global climate
patterns, stellar spectra, or human gene distributions, regularly confront the problem of …

NAMD is a parallel molecular dynamics code designed for high‐performance simulation of
large biomolecular systems. NAMD scales to hundreds of processors on high‐end parallel …