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Linear relaxation based perturbation analysis (LiRPA) for neural networks, which computes provable linear bounds of output neurons given a certain amount of input perturbation, has become a core component in robustness verification and certified defense.
Enhancing certifiable robustness via a deep model ensemble
Zhang, H., Cheng, M., and Hsieh, C.-J · 1910
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Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., and Williams, R. J · 1986
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., and Potts, C · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Tiny imagenet visual recognition challenge
Le, Y. and Yang, X · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems, 2016
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2016
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On large-batch training for deep learning: Generalization gap and sharp minima
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P. T. P · 2016
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Zagoruyko, S. and Komodakis, N · 2016
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A downsampled variant of imagenet as an alternative to the cifar datasets
Chrabaszcz, P., Loshchilov, I., and Hutter, F · 2017
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Formal verification of piece-wise linear feed-forward neural networks
Ehlers, R · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K · 2017
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Formal guarantees on the robustness of a classifier against adversarial manipulation
Hein, M. and Andriushchenko, M · 2017
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Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Hoffer, E., Hubara, I., and Soudry, D · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
Katz, G., Barrett, C., Dill, D. L., Julian, K., and Kochenderfer, M. J · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K · 2017
Cited alongside, same era.
Generating natural language adversarial examples
Alzantot, M., Sharma, Y., Elgohary, A., Ho, B.-J., Srivastava, M., and Chang, K.-W · 2018
Cited alongside, same era.
Black-box generation of adversarial text sequences to evade deep learning classifiers
Gao, J., Lanchantin, J., Soffa, M. L., and Qi, Y · 2018
Cited alongside, same era.
On the effectiveness of interval bound propagation for training verifiably robust models
Gowal, S., Dvijotham, K., Stanforth, R., Bunel, R., Qin, C., Uesato, J., Mann, T., and Kohli, P · 2018
Cited alongside, same era.
Achieving verified robustness to symbol substitutions via interval bound propagation
Huang, P.-S., Stanforth, R., Welbl, J., Dyer, C., Yogatama, D., Gowal, S., Dvijotham, K., and Kohli, P · 2019
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Certified robustness to adversarial word substitutions
Jia, R., Raghunathan, A., Göksel, K., and Liang, P · 2019
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Popqorn: Quantifying robustness of recurrent neural networks
Ko, C.-Y., Lyu, Z., Weng, T.-W., Daniel, L., Wong, N., and Lin, D · 2019
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Certified robustness to adversarial examples with differential privacy
Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., and Jana, S · 2019
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Certified adversarial robustness with additive noise
Li, B., Chen, C., Wang, W., and Carin, L · 2019
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Fastened crown: Tightened neural network robustness certificates
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Jastrzebski, S., Kenton, Z., Arpit, D., Ballas, N., Fischer, A., Bengio, Y., and Storkey, A. J · 2018
Cited alongside, same era.
Eran user manual
Maurer, J., Singh, G., Mirman, M., Gehr, T., Hoffmann, A., Tsankov, P., Cohen, D. D., and Püschel, M · 2018
Cited alongside, same era.
Differentiable abstract interpretation for provably robust neural networks
Mirman, M., Gehr, T., and Vechev, M · 2018
Cited alongside, same era.
Semidefinite relaxations for certifying robustness to adversarial examples
Raghunathan, A., Steinhardt, J., and Liang, P. S · 2018
Cited alongside, same era.
Fast and effective robustness certification
Singh, G., Gehr, T., Mirman, M., Püschel, M., and Vechev, M · 2018
Cited alongside, same era.
Towards fast computation of certified robustness for relu networks
Weng, T.-W., Zhang, H., Chen, H., Song, Z., Hsieh, C.-J., Daniel, L., Boning, D., and Dhillon, I · 2018
Cited alongside, same era.
Scaling provable adversarial defenses
Wong, E., Schmidt, F., Metzen, J. H., and Kolter, J. Z · 2018
Cited alongside, same era.
Lyu, Z., Ko, C.-Y., Kong, Z., Wong, N., Lin, D., and Daniel, L · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Evaluating robustness of neural networks with mixed integer programming
Tjeng, V., Xiao, K., and Tedrake, R · 2019
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Certifiable robustness and robust training for graph convolutional networks
Zügner, D. and Günnemann, S · 2019
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Adversarial training and provable defenses: Bridging the gap
Balunovic, M. and Vechev, M · 2020
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Robustness verification for transformers
Shi, Z., Zhang, H., Chang, K.-W., Huang, M., and Hsieh, C.-J · 2020
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Tjandraatmadja, C., Anderson, R., Huchette, J., Ma, W., Patel, K., and Vielma, J. P · 2020
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Wong, E., Schneider, T., Schmitt, J., Schmidt, F. R., and Kolter, J. Z · 2020
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Towards stable and efficient training of verifiably robust neural networks
Zhang, H., Chen, H., Xiao, C., Li, B., Boning, D., and Hsieh, C.-J · 2020
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Improving the tightness of convex relaxation bounds for training certifiably robust classifiers
Zhu, C., Ni, R., Chiang, P.-y., Li, H., Huang, F., and Goldstein, T · 2020
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