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FFT-based Gradient Sparsification for the Distributed Train..:

, In: Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing,

Wang, Linnan ; Wu, Wei ; Zhang, Junyu... - p. 113-124 , 2020

Link: https://dl.acm.org/doi/10.1145/3369583.3392681

RT 
T1 <cTitle>Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing</cTitle>: T1 <Title>FFT-based Gradient Sparsification for the Distributed Training of Deep Neural Networks</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3392681&Exemplar=1&LAN=DE
				
A1 Wang, Linnan
A1 Wu, Wei
A1 Zhang, Junyu
A1 Liu, Hang
A1 Bosilca, George
A1 Herlihy, Maurice
A1 Fonseca, Rodrigo
PB ACM
YR 2020
K1 FFT
K1 gradient compression
K1 loosy gradients
K1 machine learning
K1 neural networks
K1 Computer systems organization
K1 Architectures
K1 Other architectures
K1 Neural networks
K1 Theory of computation
K1 Theory and algorithms for application domains
K1 Machine learning theory
K1 Design and analysis of algorithms
K1 Data structures design and analysis
K1 Data compression
K1 Heterogeneous (hybrid) systems
SP 113
OP 124
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3369583.3392681
DO https://dl.acm.org/doi/10.1145/3369583.3392681
SF ELIB - SuUB Bremen

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