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Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed
  on Orbits

Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits

31 May 2023
Zhuokai Zhao
Takumi Matsuzawa
W. Irvine
Michael Maire
G. Kindlmann
ArXivPDFHTML

Papers citing "Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits"

3 / 3 papers shown
Title
On Translation Invariance in CNNs: Convolutional Layers can Exploit
  Absolute Spatial Location
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
O. Kayhan
J. C. V. Gemert
209
232
0
16 Mar 2020
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
227
3,681
0
28 Feb 2017
RenderGAN: Generating Realistic Labeled Data
RenderGAN: Generating Realistic Labeled Data
Leon Sixt
Benjamin Wild
Tim Landgraf
GAN
158
174
0
04 Nov 2016
1