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Despite the promise of Lipschitz-based methods for provably-robust deep learning with deterministic guarantees, current state-of-the-art results are limited to feed-forward Convolutional Networks (ConvNets) on low-dimensional data, such as CIFAR-10.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
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Explaining and harnessing adversarial examples, 2015
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Incorporating nesterov momentum into adam
Timothy Dozat · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Reluplex: An efficient SMT solver for verifying deep neural networks
Guy Katz, Clark W. Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Diracnets: Training very deep neural networks without skip-connections
Sergey Zagoruyko and Nikos Komodakis · 2017
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Certifiable distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
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Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks
Yusuke Tsuzuku, Issei Sato, and Masashi Sugiyama · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
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Sorting out Lipschitz function approximation
Cem Anil, James Lucas, and Roger Gross · 2019
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Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter · 2019
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Provable robustness of ReLU networks via maximization of linear regions
Francesco Croce, Maksym Andriushchenko, and Matthias Hein · 2019
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Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks
Xiaohan Ding, Yuchen Guo, Guiguang Ding, and Jungong Han · 2019
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Generalizable adversarial training via spectral normalization
Farzan Farnia, Jesse Zhang, and David Tse · 2019
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Provable certificates for adversarial examples: Fitting a ball in the union of polytopes
Matt Jordan, Justin Lewis, and Alexandros G. Dimakis · 2019
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Preventing gradient attenuation in lipschitz constrained convolutional networks
Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B Grosse, and Joern-Henrik Jacobsen · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Provably robust deep learning via adversarially trained smoothed classifiers
Hadi Salman, Jerry Li, Ilya Razenshteyn, Pengchuan Zhang, Huan Zhang, Sebastien Bubeck, and Greg Yang · 2019
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Evaluating robustness of neural networks with mixed integer programming
Vincent Tjeng, Kai Y. Xiao, and Russ Tedrake · 2019
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Lucas Beyer, Olivier J Hénaff, Alexander Kolesnikov, Xiaohua Zhai, and Aäron van den Oord · 2020
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The lipschitz constant of self-attention
Hyunjik Kim, George Papamakarios, and Andriy Mnih · 2021
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Relaxing local robustness
Klas Leino and Matt Fredrikson · 2021
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Globally-robust neural networks
Klas Leino, Zifan Wang, and Matt Fredrikson · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Pervasive label errors in test sets destabilize machine learning benchmarks
Curtis G Northcutt, Anish Athalye, and Jonas Mueller · 2021
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Skew orthogonal convolutions
Sahil Singla and Soheil Feizi · 2021
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Lipschitz-certifiable training with a tight outer bound
Sungyoon Lee, Jaewook Lee, and Saerom Park · 2020
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Denoised smoothing: A provable defense for pretrained classifiers
Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, and J Zico Kolter · 2020
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Is normalization indispensable for training deep neural network?
Jie Shao, Kai Hu, Changhu Wang, Xiangyang Xue, and Bhiksha Raj · 2020
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A closer look at accuracy vs. robustness
Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Russ R. Salakhutdinov, and Kamalika Chaudhuri · 2020
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Orthogonalizing convolutional layers with the cayley transform
Asher Trockman and J Zico Kolter · 2021
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Re-labeling imagenet: From single to multi-labels, from global to localized labels
Sangdoo Yun, Seong Joon Oh, Byeongho Heo, Dongyoon Han, Junsuk Choe, and Sanghyuk Chun · 2021
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(certified!!) adversarial robustness for free!
Nicholas Carlini, Florian Tramer, J Zico Kolter, et al · 2022
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Repmlpnet: Hierarchical vision mlp with re-parameterized locality
Xiaohan Ding, Honghao Chen, Xiangyu Zhang, Jungong Han, and Guiguang Ding · 2022
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A dynamical system perspective for Lipschitz neural networks
Laurent Meunier, Blaise J Delattre, Alexandre Araujo, and Alexandre Allauzen · 2022
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Improved deterministic l2 robustness on cifar-10 and cifar-100
Sahil Singla, Surbhi Singla, and Soheil Feizi · 2022
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Resmlp: Feedforward networks for image classification with data-efficient training
Hugo Touvron, Piotr Bojanowski, Mathilde Caron, Matthieu Cord, Alaaeldin El-Nouby, Edouard Grave, Gautier Izacard, Armand Joulin, Gabriel Synnaeve, Jakob Verbeek, et al · 2022
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When does dough become a bagel? analyzing the remaining mistakes on imagenet
Vijay Vasudevan, Benjamin Caine, Raphael Gontijo-Lopes, Sara Fridovich-Keil, and Rebecca Roelofs · 2022
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Lot: Layer-wise orthogonal training on improving l2 certified robustness
Xiaojun Xu, Linyi Li, and Bo Li · 2022
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Metaformer is actually what you need for vision
Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, and Shuicheng Yan · 2022
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A unified algebraic perspective on lipschitz neural networks
Alexandre Araujo, Aaron J Havens, Blaise Delattre, Alexandre Allauzen, and Bin Hu · 2023
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Limitations of piecewise linearity for efficient robustness certification
Klas Leino · 2023
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