pPython seeks to provide a parallel capability that provides good speed-...
Defending community-owned cyber space requires community-based efforts.
...
Matrix/array analysis of networks can provide significant insight into t...
As research and practice in artificial intelligence (A.I.) grow in leaps...
This paper updates the survey of AI accelerators and processors from pas...
Internet analysis is a major challenge due to the volume and rate of net...
In this paper we address the application of pre-processing techniques to...
Modern network sensors continuously produce enormous quantities of raw d...
Python has become a standard scientific computing language with fast-gro...
pPython seeks to provide a parallel capability that provides good speed-...
High-Performance Computing (HPC) centers and cloud providers support an
...
Long range detection is a cornerstone of defense in many operating domai...
The Internet has become a critical component of modern civilization requ...
Deep learning (DL) workflows demand an ever-increasing budget of compute...
Adversarial Internet robots (botnets) represent a growing threat to the ...
The distribution gap between training datasets and data encountered in
p...
With the recognition of cyberspace as an operating domain, concerted eff...
Over the past several years, new machine learning accelerators were bein...
Supercomputers are complex systems producing vast quantities of performa...
First responders and other forward deployed essential workers can benefi...
AI algorithms that identify maneuvers from trajectory data could play an...
Diverse workloads such as interactive supercomputing, big data analysis,...
The Internet has never been more important to our society, and understan...
Hypersparse matrices are a powerful enabler for a variety of network, he...
Artificial intelligence (AI) and Machine learning (ML) workloads are an
...
Boosting is a method for finding a highly accurate hypothesis by linearl...
This paper proposes discomfort as a new material for HCI researchers and...
Model compression methods are important to allow for easier deployment o...
Several methods of knowledge distillation have been developed for neural...
Knowledge distillation (KD) is a general deep neural network training
fr...
New machine learning accelerators are being announced and released each ...
Deep neural networks have shown great success in many diverse fields. Th...
Artificial Intelligence/Machine Learning applications require the traini...
Rapid launch of thousands of jobs is essential for effective interactive...
A Multigrid Full Approximation Storage algorithm for solving Deep Residu...
Pandemic measures such as social distancing and contact tracing can be
e...
Modern face alignment methods have become quite accurate at predicting t...
The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches t...
The rise of graph analytic systems has created a need for new ways to me...
The SuiteSparse GraphBLAS C-library implements high performance hyperspa...
In this paper, we present a novel and new file-based communication
archi...
The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches t...
Advances in multicore processors and accelerators have opened the flood ...
Federated authentication can drastically reduce the overhead of basic ac...
The Intel Xeon Phi manycore processor is designed to provide high perfor...
The Dynamic Distributed Dimensional Data Model (D4M) library implements
...
Face super-resolution methods usually aim at producing visually appealin...
For decades, the use of HPC systems was limited to those in the physical...
Progress in video anomaly detection research is currently slowed by smal...
Simulation, machine learning, and data analysis require a wide range of
...