363 citations · 368 across the 5 of their papers we have counts for
7 papers
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…
Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks
Theodore Papamarkou, Hayley Guy, Bryce Kroencke +8
Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability…
Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks
Mihaela Dimovska, Travis Johnston, Catherine D. Schuman +2
Inspired by the connectivity mechanisms in the brain, neuromorphic computing architectures model Spiking Neural Networks (SNNs) in silicon. As such, neuromorphic architectures are…
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…
Spike-based primitives for graph algorithms
Kathleen E. Hamilton, Tiffany M. Mintz, Catherine D. Schuman
In this paper we consider graph algorithms and graphical analysis as a new application for neuromorphic computing platforms. We demonstrate how the nonlinear dynamics of spiking ne…
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…