1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.DC2023★ 1 cited
GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism
Sandeep Polisetty, Juelin Liu, Kobi Falus +4
Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their superior performance in various graph analytical tasks. Mini…
cs.LG2019
In-Place Zero-Space Memory Protection for CNN
Hui Guan, Lin Ning, Zhen Lin +3
Convolutional Neural Networks (CNN) are being actively explored for safety-critical applications such as autonomous vehicles and aerospace, where it is essential to ensure the reli…
cs.LG2019
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…