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Fast and Private Inference of Deep Neural Networks by Co-designing
  Activation Functions

Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions

14 June 2023
Abdulrahman Diaa
L. Fenaux
Thomas Humphries
Marian Dietz
Faezeh Ebrahimianghazani
Bailey Kacsmar
Xinda Li
Nils Lukas
Rasoul Akhavan Mahdavi
Simon Oya
Ehsan Amjadian
Florian Kerschbaum
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Papers citing "Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions"

3 / 3 papers shown
Title
Regularized PolyKervNets: Optimizing Expressiveness and Efficiency for
  Private Inference in Deep Neural Networks
Regularized PolyKervNets: Optimizing Expressiveness and Efficiency for Private Inference in Deep Neural Networks
Toluwani Aremu
25
0
0
23 Dec 2023
Compact: Approximating Complex Activation Functions for Secure
  Computation
Compact: Approximating Complex Activation Functions for Secure Computation
Mazharul Islam
Sunpreet S. Arora
Rahul Chatterjee
Peter Rindal
Maliheh Shirvanian
32
4
0
09 Sep 2023
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU
Sijun Tan
Brian Knott
Yuan Tian
David J. Wu
BDL
FedML
57
183
0
22 Apr 2021
1