363 citations · 363 across the 1 of their papers we have counts for
5 papers
Exascale Deep Learning to Accelerate Cancer Research
Robert M. Patton, J. Travis Johnston, Steven R. Young +9
Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural n…
Unsupervised Identification of Study Descriptors in Toxicology Research: An Experimental Study
Drahomira Herrmannova, Steven R. Young, Robert M. Patton +3
Identifying and extracting data elements such as study descriptors in publication full texts is a critical yet manual and labor-intensive step required in a number of tasks. In thi…
Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment
G. N. Perdue, A. Ghosh, M. Wospakrik +53
We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate…
Do Citations and Readership Identify Seminal Publications?
Drahomira Herrmannova, Robert M. Patton, Petr Knoth +1
In this paper, we show that citation counts work better than a random baseline (by a margin of 10%) in distinguishing excellent research, while Mendeley reader counts don't work be…
A Survey of Neuromorphic Computing and Neural Networks in Hardware
Catherine D. Schuman, Thomas E. Potok, Robert M. Patton +4
Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologica…