363 citations · 412 across the 19 of their papers we have counts for
4 papers · 1 filter
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…
Stochasticity and Robustness in Spiking Neural Networks
Wilkie Olin-Ammentorp, Karsten Beckmann, Catherine D. Schuman +2
Artificial neural networks normally require precise weights to operate, despite their origins in biological systems, which can be highly variable and noisy. When implementing artif…
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…