1.4k citations · 7.6k across the 74 of their papers we have counts for
75 papers
Self-Compressing Neural Networks
Szabolcs Cséfalvay, James Imber
This work focuses on reducing neural network size, which is a major driver of neural network execution time, power consumption, bandwidth, and memory footprint. A key challenge is…
Neutron Tagging following Atmospheric Neutrino Events in a Water Cherenkov Detector
K. Abe, Y. Haga, Y. Hayato +301
We present the development of neutron-tagging techniques in Super-Kamiokande IV using a neural network analysis. The detection efficiency of neutron capture on hydrogen is estimate…
Testing Non-Standard Interactions Between Solar Neutrinos and Quarks with Super-Kamiokande
Kamiokande Collaboration, P. Weatherly, K. Abe +268
Non-Standard Interactions (NSI) between neutrinos and matter affect the neutrino flavor oscillations. Due to the high matter density in the core of the Sun, solar neutrinos are sui…
Indirect Search for Dark Matter from the Galactic Center and Halo with the Super-Kamiokande Detector
Kamiokande Collaboration, K. Abe, C. Bronner +269
We present a search for an excess of neutrino interactions due to dark matter in the form of Weakly Interacting Massive Particles (WIMPs) annihilating in the galactic center or hal…
ISP4ML: Understanding the Role of Image Signal Processing in Efficient Deep Learning Vision Systems
Patrick Hansen, Alexey Vilkin, Yury Khrustalev +4
Convolutional neural networks (CNNs) are now predominant components in a variety of computer vision (CV) systems. These systems typically include an image signal processor (ISP), e…
Sensitivity of Super-Kamiokande with Gadolinium to Low Energy Anti-neutrinos from Pre-supernova Emission
C. Simpson, K. Abe, C. Bronner +186
Supernova detection is a major objective of the Super-Kamiokande (SK) experiment. In the next stage of SK (SK-Gd), gadolinium (Gd) sulfate will be added to the detector, which will…