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Adversarial Robustness Guarantees for Random Deep Neural Networks

Adversarial Robustness Guarantees for Random Deep Neural Networks

13 April 2020
Giacomo De Palma
B. Kiani
S. Lloyd
    AAML
    OOD
ArXivPDFHTML

Papers citing "Adversarial Robustness Guarantees for Random Deep Neural Networks"

6 / 6 papers shown
Title
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural
  Networks
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks
Yunzhen Feng
Tim G. J. Rudner
Nikolaos Tsilivis
Julia Kempe
AAML
BDL
43
1
0
27 Apr 2024
Evolution of Neural Tangent Kernels under Benign and Adversarial
  Training
Evolution of Neural Tangent Kernels under Benign and Adversarial Training
Noel Loo
Ramin Hasani
Alexander Amini
Daniela Rus
AAML
28
12
0
21 Oct 2022
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train
  10,000-Layer Vanilla Convolutional Neural Networks
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Lechao Xiao
Yasaman Bahri
Jascha Narain Sohl-Dickstein
S. Schoenholz
Jeffrey Pennington
220
348
0
14 Jun 2018
Adversarial examples from computational constraints
Adversarial examples from computational constraints
Sébastien Bubeck
Eric Price
Ilya P. Razenshteyn
AAML
62
230
0
25 May 2018
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
272
5,833
0
08 Jul 2016
The Loss Surfaces of Multilayer Networks
The Loss Surfaces of Multilayer Networks
A. Choromańska
Mikael Henaff
Michaël Mathieu
Gerard Ben Arous
Yann LeCun
ODL
177
1,185
0
30 Nov 2014
1