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Training Provably Robust Models by Polyhedral Envelope Regularization
v1v2v3 (latest)

Training Provably Robust Models by Polyhedral Envelope Regularization

IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019
10 December 2019
Chen Liu
Mathieu Salzmann
Sabine Süsstrunk
    AAML
ArXiv (abs)PDFHTML

Papers citing "Training Provably Robust Models by Polyhedral Envelope Regularization"

5 / 5 papers shown
On Continuity of Robust and Accurate Classifiers
On Continuity of Robust and Accurate Classifiers
Ramin Barati
Reza Safabakhsh
Mohammad Rahmati
AAML
364
1
0
29 Sep 2023
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A
  Contemporary Survey
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
Yulong Wang
Tong Sun
Shenghong Li
Xinnan Yuan
W. Ni
Ekram Hossain
H. Vincent Poor
AAML
276
29
0
11 Mar 2023
On the Robustness of Bayesian Neural Networks to Adversarial Attacks
On the Robustness of Bayesian Neural Networks to Adversarial AttacksIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022
Luca Bortolussi
Ginevra Carbone
Luca Laurenti
A. Patané
G. Sanguinetti
Matthew Wicker
AAML
267
14
0
13 Jul 2022
Understanding Adversarial Robustness from Feature Maps of Convolutional
  Layers
Understanding Adversarial Robustness from Feature Maps of Convolutional LayersIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022
Cong Xu
Wei Zhang
Jun Wang
Min Yang
AAML
158
2
0
25 Feb 2022
The Fundamental Limits of Interval Arithmetic for Neural Networks
The Fundamental Limits of Interval Arithmetic for Neural Networks
M. Mirman
Maximilian Baader
Martin Vechev
132
8
0
09 Dec 2021
1