Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …

Convolutional neural networks (CNNs) have shown outstanding results in different
application tasks. However, the best performance is obtained when customized CNNs …

Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …

Deep learning is a promising tool to determine the physical model that describes our
universe. To handle the considerable computational cost of this problem, we present …

Final-state kinematic imbalances are measured in mesonless production of ν μ+ A→ μ-+ p+
X in the MINERvA tracker. Initial-and final-state nuclear effects are probed using the …

We introduce Deep500: the first customizable benchmarking infrastructure that enables fair
comparison of the plethora of deep learning frameworks, algorithms, libraries, and …

To reduce the computational complexity of training a deep neural network architecture using
large data sets of 3D scenes, a competitive learning scheme was devised. The proposed …

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by interrupted
breathing during sleep. Because of the cost, complexity, and accessibility issue related to …

Consensus-based distributed optimization methods have recently been advocated as
alternatives to parameter server and ring all-reduce paradigms for large scale training of …

Cancer is a complex disease, the understanding and treatment of which are being aided
through increases in the volume of collected data and in the scale of deployed computing …