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An Analysis of the Expressiveness of Deep Neural Network Architectures
  Based on Their Lipschitz Constants

An Analysis of the Expressiveness of Deep Neural Network Architectures Based on Their Lipschitz Constants

24 December 2019
Siqi Zhou
Angela P. Schoellig
ArXivPDFHTML

Papers citing "An Analysis of the Expressiveness of Deep Neural Network Architectures Based on Their Lipschitz Constants"

3 / 3 papers shown
Title
When Deep Learning Meets Polyhedral Theory: A Survey
When Deep Learning Meets Polyhedral Theory: A Survey
Joey Huchette
Gonzalo Muñoz
Thiago Serra
Calvin Tsay
AI4CE
102
33
0
29 Apr 2023
Robustness in deep learning: The good (width), the bad (depth), and the
  ugly (initialization)
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)
Zhenyu Zhu
Fanghui Liu
Grigorios G. Chrysos
V. Cevher
44
19
0
15 Sep 2022
Exploring Architectural Ingredients of Adversarially Robust Deep Neural
  Networks
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
Hanxun Huang
Yisen Wang
S. Erfani
Quanquan Gu
James Bailey
Xingjun Ma
AAML
TPM
48
100
0
07 Oct 2021
1