To address a common data flow problem in the field of machine learning, we will develop multilevel methods for increasing the efficiency of neural network training processes that readily scale-up to accommodate large-scale problems. Applicable across many domains, this research supports scientists and analysts in fields that employ neural networks to analyze simulation results (nuclear stockpile stewardship, high-energy-density physics) and multimodal data (counterterrorism, bioscience) as a basis for their predictions.
Ponce, C., et al. 2019. "Multilevel Methods for Neural Networks." LLNL-PRES-769948.
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