ChainzRule with DREG regularization claims 15.5x fewer parameters than standard models, 23.1% lower peak gradient volatility on MNIST, and 70.17% accuracy on Yelp Full ordinal regression.
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Neural network reconstructs cosmic-ray trajectories to better than 1.4° angular resolution and separates charges to >95% accuracy for Z≤8 using Geant4-simulated data for the RadMap Telescope.
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Layer-wise Derivative Controlled Networks
ChainzRule with DREG regularization claims 15.5x fewer parameters than standard models, 23.1% lower peak gradient volatility on MNIST, and 70.17% accuracy on Yelp Full ordinal regression.
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A Neural-Network Framework for Tracking and Identification of Cosmic-Ray Nuclei in the RadMap Telescope
Neural network reconstructs cosmic-ray trajectories to better than 1.4° angular resolution and separates charges to >95% accuracy for Z≤8 using Geant4-simulated data for the RadMap Telescope.