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Adversarial Examples, Uncertainty, and Transfer Testing Robustness in
  Gaussian Process Hybrid Deep Networks

Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks

8 July 2017
John Bradshaw
A. G. Matthews
Zoubin Ghahramani
    BDL
    AAML
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Papers citing "Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks"

4 / 4 papers shown
Title
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any
  Architecture are Gaussian Processes
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
Greg Yang
9
189
0
28 Oct 2019
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
245
5,361
0
08 Jul 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
240
8,157
0
06 Jun 2015
Manifold Gaussian Processes for Regression
Manifold Gaussian Processes for Regression
Roberto Calandra
Jan Peters
C. Rasmussen
M. Deisenroth
74
255
0
24 Feb 2014
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