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Differentially private training of residual networks with scale
  normalisation

Differentially private training of residual networks with scale normalisation

1 March 2022
Helena Klause
Alexander Ziller
Daniel Rueckert
Kerstin Hammernik
Georgios Kaissis
ArXivPDFHTML

Papers citing "Differentially private training of residual networks with scale normalisation"

10 / 10 papers shown
Title
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
Kristian Schwethelm
Johannes Kaiser
Jonas Kuntzer
Mehmet Yigitsoy
Daniel Rueckert
Georgios Kaissis
37
0
0
01 Oct 2024
Differentially Private Image Classification by Learning Priors from
  Random Processes
Differentially Private Image Classification by Learning Priors from Random Processes
Xinyu Tang
Ashwinee Panda
Vikash Sehwag
Prateek Mittal
23
20
0
08 Jun 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential
  Privacy
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Natalia Ponomareva
Hussein Hazimeh
Alexey Kurakin
Zheng Xu
Carson E. Denison
H. B. McMahan
Sergei Vassilvitskii
Steve Chien
Abhradeep Thakurta
94
167
0
01 Mar 2023
Private, fair and accurate: Training large-scale, privacy-preserving AI
  models in medical imaging
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
Soroosh Tayebi Arasteh
Alexander Ziller
Christiane Kuhl
Marcus R. Makowski
S. Nebelung
R. Braren
Daniel Rueckert
Daniel Truhn
Georgios Kaissis
MedIm
37
17
0
03 Feb 2023
Differentially Private Image Classification from Features
Differentially Private Image Classification from Features
Harsh Mehta
Walid Krichene
Abhradeep Thakurta
Alexey Kurakin
Ashok Cutkosky
52
7
0
24 Nov 2022
Bridging the Gap: Differentially Private Equivariant Deep Learning for
  Medical Image Analysis
Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis
Florian A. Hölzl
Daniel Rueckert
Georgios Kaissis
FedML
MedIm
29
1
0
09 Sep 2022
Not all noise is accounted equally: How differentially private learning
  benefits from large sampling rates
Not all noise is accounted equally: How differentially private learning benefits from large sampling rates
Friedrich Dörmann
Osvald Frisk
L. Andersen
Christian Fischer Pedersen
FedML
59
25
0
12 Oct 2021
ResNet strikes back: An improved training procedure in timm
ResNet strikes back: An improved training procedure in timm
Ross Wightman
Hugo Touvron
Hervé Jégou
AI4TS
212
487
0
01 Oct 2021
Opacus: User-Friendly Differential Privacy Library in PyTorch
Opacus: User-Friendly Differential Privacy Library in PyTorch
Ashkan Yousefpour
I. Shilov
Alexandre Sablayrolles
Davide Testuggine
Karthik Prasad
...
Sayan Gosh
Akash Bharadwaj
Jessica Zhao
Graham Cormode
Ilya Mironov
VLM
159
350
0
25 Sep 2021
Tempered Sigmoid Activations for Deep Learning with Differential Privacy
Tempered Sigmoid Activations for Deep Learning with Differential Privacy
Nicolas Papernot
Abhradeep Thakurta
Shuang Song
Steve Chien
Ulfar Erlingsson
AAML
147
178
0
28 Jul 2020
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