Fetching the paper…
Reading the bibliography…
The Lipschitz constant of neural networks plays an important role in several contexts of deep learning ranging from robustness certification and regularization to stability analysis of systems with neural network controllers.
Robust large margin deep neural networks
Jure Sokolić, Raja Giryes, Guillermo Sapiro, and Miguel RD Rodrigues · 2017
Earlier work this paper cites.
Spectral normalization for generative adversarial networks, 2018
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Earlier work this paper cites.
Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks, 2018
Yusuke Tsuzuku, Issei Sato, and Masashi Sugiyama · 2018
Earlier work this paper cites.
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Aladin Virmaux and Kevin Scaman · 2018
Earlier work this paper cites.
Efficient formal safety analysis of neural networks
Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, and Suman Jana · 2018
Earlier work this paper cites.
Formal security analysis of neural networks using symbolic intervals
Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, and Suman Jana · 2018
Cited alongside, same era.
Towards fast computation of certified robustness for ReLU networks
Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Duane Boning, Inderjit S. Dhillon, and Luca Daniel · 2018
Cited alongside, same era.
Evaluating the robustness of neural networks: An extreme value theory approach, 2018
Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, and Luca Daniel · 2018
Cited alongside, same era.
Invertible residual networks
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, and Joern-Henrik Jacobsen · 2019
Cited alongside, same era.
Efficient and accurate estimation of Lipschitz constants for deep neural networks
Mahyar Fazlyab, Alexander Robey, Hamed Hassani, Manfred Morari, and George J. Pappas · 2019
Residual flows for invertible generative modeling, 2020
Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, and Jörn-Henrik Jacobsen · 2020
Later among the works it cites.
Exactly computing the local Lipschitz constant of ReLU networks, 2020
Matt Jordan and Alexandros G. Dimakis · 2020
Later among the works it cites.
The Lipschitz constant of self-attention, 2020
Hyunjik Kim, George Papamakarios, and Andriy Mnih · 2020
Later among the works it cites.
Lipschitz constant estimation of neural networks via sparse polynomial optimization, 2020
Fabian Latorre, Paul Rolland, and Volkan Cevher · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Computational optimal transport: With applications to data science
Gabriel Peyré and Marco Cuturi
Cited in the paper.