38 citations · 64 across the 10 of their papers we have counts for
12 papers
ICDARTS: Improving the Stability and Performance of Cyclic DARTS
Emily Herron, Derek Rose, Steven Young
This work introduces improvements to the stability and generalizability of Cyclic DARTS (CDARTS). CDARTS is a Differentiable Architecture Search (DARTS)-based approach to neural ar…
Vertex finding in neutrino-nucleus interaction: A Model Architecture Comparison
F. Akbar, A. Ghosh, S. Young +48
We compare different neural network architectures for Machine Learning (ML) algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The ar…
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment
Maryam Parsa, Catherine D. Schuman, Prasanna Date +8
Training neural networks for neuromorphic deployment is non-trivial. There have been a variety of approaches proposed to adapt back-propagation or back-propagation-like algorithms…
Inferring Convolutional Neural Networks' accuracies from their architectural characterizations
Duc Hoang, Jesse Hamer, Gabriel N. Perdue +3
Convolutional Neural Networks (CNNs) have shown strong promise for analyzing scientific data from many domains including particle imaging detectors. However, the challenge of choos…
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…
Deep Modulation (Deepmod): A Self-Taught PHY Layer for Resilient Digital Communications
Adam Anderson, Steven R. Young, F. Kyle Reed +1
Traditional physical (PHY) layer protocols contain chains of signal processing blocks that have been mathematically optimized to transmit information bits efficiently over noisy ch…
Deep Learning for Vertex Reconstruction of Neutrino-Nucleus Interaction Events with Combined Energy and Time Data
Linghao Song, Fan Chen, Steven R. Young +3
We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics. In this approach, we combine b…
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…
Data Mining for better material synthesis: the case of pulsed laser deposition of complex oxides
Steven R. Young, Artem Maksov, Maxim Ziatdinov +8
The pursuit of more advanced electronics, finding solutions to energy needs, and tackling a wealth of social issues often hinges upon the discovery and optimization of new function…
A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers
Thomas E. Potok, Catherine Schuman, Steven R. Young +6
Current Deep Learning approaches have been very successful using convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers. Three limita…
Recurrent Online Clustering as a Spatio-Temporal Feature Extractor in DeSTIN
Steven R. Young, Itamar Arel
This paper presents a basic enhancement to the DeSTIN deep learning architecture by replacing the explicitly calculated transition tables that are used to capture temporal features…