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2105.11417
Cited By
Skew Orthogonal Convolutions
24 May 2021
Sahil Singla
S. Feizi
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Papers citing
"Skew Orthogonal Convolutions"
18 / 18 papers shown
Title
Compositional Curvature Bounds for Deep Neural Networks
Taha Entesari
Sina Sharifi
Mahyar Fazlyab
AAML
42
0
0
07 Jun 2024
Graph Unitary Message Passing
Haiquan Qiu
Yatao Bian
Quanming Yao
37
2
0
17 Mar 2024
Monotone, Bi-Lipschitz, and Polyak-Lojasiewicz Networks
Ruigang Wang
Krishnamurthy Dvijotham
I. Manchester
36
5
0
02 Feb 2024
1-Lipschitz Neural Networks are more expressive with N-Activations
Bernd Prach
Christoph H. Lampert
AAML
FAtt
24
0
0
10 Nov 2023
Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
Mahyar Fazlyab
Taha Entesari
Aniket Roy
Ramalingam Chellappa
AAML
16
11
0
29 Sep 2023
Certifying LLM Safety against Adversarial Prompting
Aounon Kumar
Chirag Agarwal
Suraj Srinivas
Aaron Jiaxun Li
S. Feizi
Himabindu Lakkaraju
AAML
27
165
0
06 Sep 2023
Robust low-rank training via approximate orthonormal constraints
Dayana Savostianova
Emanuele Zangrando
Gianluca Ceruti
Francesco Tudisco
26
9
0
02 Jun 2023
A Unified Algebraic Perspective on Lipschitz Neural Networks
Alexandre Araujo
Aaron J. Havens
Blaise Delattre
A. Allauzen
Bin Hu
AAML
36
52
0
06 Mar 2023
Orthogonal SVD Covariance Conditioning and Latent Disentanglement
Yue Song
N. Sebe
Wei Wang
33
6
0
11 Dec 2022
Improved techniques for deterministic l2 robustness
Sahil Singla
S. Feizi
AAML
23
9
0
15 Nov 2022
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks
Bernd Prach
Christoph H. Lampert
32
35
0
05 Aug 2022
Improving Covariance Conditioning of the SVD Meta-layer by Orthogonality
Yue Song
N. Sebe
Wei Wang
19
8
0
05 Jul 2022
Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis
Raphael Ettedgui
Alexandre Araujo
Rafael Pinot
Y. Chevaleyre
Jamal Atif
AAML
34
3
0
03 Jun 2022
projUNN: efficient method for training deep networks with unitary matrices
B. Kiani
Randall Balestriero
Yann LeCun
S. Lloyd
43
32
0
10 Mar 2022
Mutual Adversarial Training: Learning together is better than going alone
Jiang-Long Liu
Chun Pong Lau
Hossein Souri
S. Feizi
Ramalingam Chellappa
OOD
AAML
43
24
0
09 Dec 2021
Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks
El Mehdi Achour
Franccois Malgouyres
Franck Mamalet
16
20
0
12 Aug 2021
Policy Smoothing for Provably Robust Reinforcement Learning
Aounon Kumar
Alexander Levine
S. Feizi
AAML
20
55
0
21 Jun 2021
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
244
349
0
14 Jun 2018
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