Lipschitz regularity of deep neural networks: analysis and efficient estimation

Praktische verfahren der gleichungsauflösung

RV Mises and Hilda Pollaczek-Geiringer · 1929

Earlier work this paper cites.

Geometric measure theory

Herbert Federer · 1969

Earlier work this paper cites.

The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors

Seppo Linnainmaa · 1970

Earlier work this paper cites.

Automatic Differentiation: Techniques and Applications

Louis B. Rall · 1981

Earlier work this paper cites.

On Differentiating Eigenvalues and Eigenvectors

Jan R. Magnus · 1985

Earlier work this paper cites.

Distance–based classification with lipschitz functions

Ulrike von Luxburg and Olivier Bousquet · 2004

Earlier work this paper cites.

Optimal transport: old and new

Cédric Villani · 2008

Earlier work this paper cites.

Evaluating derivatives: principles and techniques of algorithmic differentiation

Andreas Griewank and Andrea Walther · 2008

Earlier work this paper cites.

Imagenet classification with deep convolutional neural networks

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012

Earlier work this paper cites.

Linguistic regularities in continuous space word representations

Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig · 2013

Earlier work this paper cites.

Handbook of global optimization

Original

Reiner Horst and Panos M Pardalos · 2013

Cited alongside, same era.

Towards end-to-end speech recognition with recurrent neural networks

Alex Graves and Navdeep Jaitly · 2014

Cited alongside, same era.

Intriguing properties of neural networks

Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014

Cited alongside, same era.

Explaining and harnessing adversarial examples

Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015

Cited alongside, same era.

TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, 2015

Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015

Attention is All You Need

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, łukasz Kaiser, and Illia Polosukhin · 2017

Later among the works it cites.

Wasserstein generative adversarial networks

Martín Arjovsky, Soumith Chintala, and Léon Bottou · 2017

Later among the works it cites.

Improved training of Wasserstein GANs

Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017

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Spectrally-normalized margin bounds for neural networks

Peter L. Bartlett, Dylan J. Foster, and Matus J. Telgarsky · 2017

Later among the works it cites.

Exploring generalization in deep learning

Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro · 2017

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Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017