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We introduce a meta-learning algorithm for adversarially robust classification.
The comparison and evaluation of forecasters
DeGroot, M. H. and Fienberg, S. E · 1983
Earlier work this paper cites.
Predicting good probabilities with supervised learning
Niculescu-Mizil, A. and Caruana, R · 2005
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
The mnist database of handwritten digit images for machine learning research
Deng, L · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Intriguing properties of neural networks, 2014
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
Obtaining well calibrated probabilities using bayesian binning
Naeini, M. P., Cooper, G., and Hauskrecht, M · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P. D., Wu, X., Jha, S., and Swami, A · 2016
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D. A · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Earlier work this paper cites.
Adversarial examples in the physical world
Kurakin, A., Goodfellow, I. J., and Bengio, S · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Earlier work this paper cites.
Universal adversarial perturbations
Moosavi-Dezfooli, S., Fawzi, A., Fawzi, O., and Frossard, P · 2017
Earlier work this paper cites.
Practical black-box attacks against machine learning
Papernot, N., McDaniel, P. D., Goodfellow, I. J., Jha, S., Celik, Z. B., and Swami, A · 2017
Earlier work this paper cites.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D. A · 2018
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q · 2018
Cited alongside, same era.
A spectral view of adversarially robust features
Garg, S., Sharan, V., Zhang, B. H., and Valiant, G · 2018
Cited alongside, same era.
Fairness without demographics in repeated loss minimization
Hashimoto, T. B., Srivastava, M., Namkoong, H., and Liang, P · 2018
Cited alongside, same era.
Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Hongler, C., and Gabriel, F · 2018
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Robustbench: a standardized adversarial robustness benchmark
Croce, F., Andriushchenko, M., Sehwag, V., Debenedetti, E., Flammarion, N., Chiang, M., Mittal, P., and Hein, M · 2020
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Training independent subnetworks for robust prediction
Havasi, M., Jenatton, R., Fort, S., Liu, J. Z., Snoek, J., Lakshminarayanan, B., Dai, A. M., and Tran, D · 2020
Later among the works it cites.
Neural tangents: Fast and easy infinite neural networks in python
Novak, R., Xiao, L., Hron, J., Lee, J., Alemi, A. A., Sohl-Dickstein, J., and Schoenholz, S. S · 2020
Later among the works it cites.
Fourier features let networks learn high frequency functions in low dimensional domains
Tancik, M., Srinivasan, P. P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J. T., and Ng, R · 2020
Later among the works it cites.
Hyperparameter ensembles for robustness and uncertainty quantification
Wenzel, F., Snoek, J., Tran, D., and Jenatton, R · 2020
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Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Cited alongside, same era.
Technical report on the cleverhans v2.1.0 adversarial examples library
Papernot, N., Faghri, F., Carlini, N., Goodfellow, I., Feinman, R., Kurakin, A., Xie, C., Sharma, Y., Brown, T., Roy, A., Matyasko, A., Behzadan, V., Hambardzumyan, K., Zhang, Z., Juang, Y.-L., Li, Z., Sheatsley, R., Garg, A., Uesato, J., Gierke, W., Dong, Y., Berthelot, D., Hendricks, P., Rauber, J., and Long, R · 2018
Cited alongside, same era.
Certifying some distributional robustness with principled adversarial training
Sinha, A., Namkoong, H., and Duchi, J. C · 2018
Cited alongside, same era.
Generalizing to unseen domains via adversarial data augmentation
Volpi, R., Namkoong, H., Sener, O., Duchi, J. C., Murino, V., and Savarese, S · 2018
Cited alongside, same era.
On exact computation with an infinitely wide neural net
Arora, S., Du, S. S., Hu, W., Li, Z., Salakhutdinov, R., and Wang, R · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T. G · 2019
Cited alongside, same era.
Adversarial examples are not bugs, they are features
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A · 2019
Cited alongside, same era.
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Fast is better than free: Revisiting adversarial training
Wong, E., Rice, L., and Kolter, J. Z · 2020
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Fast is better than free: Revisiting adversarial training
Wong, E., Rice, L., and Kolter, J. Z · 2020
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Adversarially robust low dimensional representations
Awasthi, P., Chatziafratis, V., Chen, X., and Vijayaraghavan, A · 2021
Later among the works it cites.
Neural architecture search on imagenet in four GPU hours: A theoretically inspired perspective
Chen, W., Gong, X., and Wang, Z · 2021
Later among the works it cites.
Mind the box: l 1 l_{1} -apgd for sparse adversarial attacks on image classifiers
Croce, F. and Hein, M · 2021
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Dataset meta-learning from kernel ridge-regression
Nguyen, T., Chen, Z., and Lee, J · 2021
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Deep learning on a data diet: Finding important examples early in training
Paul, M., Ganguli, S., and Dziugaite, G. K · 2021
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Neural tangent generalization attacks
Yuan, C.-H. and Wu, S.-H · 2021
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Feature purification: How adversarial training performs robust deep learning
Allen-Zhu, Z. and Li, Y · 2022
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Lottery tickets on a data diet: Finding initializations with sparse trainable networks
Paul, M., Larsen, B. W., Ganguli, S., Frankle, J., and Dziugaite, G. K · 2022
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Beyond neural scaling laws: beating power law scaling via data pruning
Sorscher, B., Geirhos, R., Shekhar, S., Ganguli, S., and Morcos, A. S · 2022
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What can the neural tangent kernel tell us about adversarial robustness?
Tsilivis, N. and Kempe, J · 2022
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