Fetching the paper…
Reading the bibliography…
Deep learning models have achieved great success in many fields, yet they are vulnerable to adversarial examples.
Binary codes capable of correcting deletions, insertions, and reversals
Levenshtein, V. I. et al · 1966
Earlier work this paper cites.
Autonomy
Aldrich, J · 1989
Earlier work this paper cites.
A theory of causal learning in children: causal maps and bayes nets
Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T., and Danks, D · 2004
Earlier work this paper cites.
Causality in economics and econometrics
Hoover, K. D · 2008
Earlier work this paper cites.
Causal inference in statistics: An overview
Pearl, J · 2009
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Maas, A., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., and Potts, C · 2011
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-r., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., et al · 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
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
Earlier work this paper cites.
Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 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.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J · 2015
Earlier work this paper cites.
Deep learning , volume 1
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y · 2016
Earlier work this paper cites.
Adversarial examples in the physical world, 2016
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
Earlier work this paper cites.
The limitations of deep learning in adversarial settings
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., and Swami, A · 2016
Earlier work this paper cites.
Deceiving google’s perspective api built for detecting toxic comments
Hosseini, H., Kannan, S., Zhang, B., and Poovendran, R · 2017
Earlier work this paper cites.
Adversarial examples for evaluating reading comprehension systems
Jia, R. and Liang, P · 2017
Earlier work this paper cites.
Adversarial training methods for semi-supervised text classification
Miyato, T., Dai, A. M., and Goodfellow, I · 2017
Earlier work this paper cites.
Elements of causal inference: foundations and learning algorithms
Peters, J., Janzing, D., and Schölkopf, B · 2017
Earlier work this paper cites.
Style transfer from non-parallel text by cross-alignment
Shen, T., Lei, T., Barzilay, R., and Jaakkola, T · 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, L., and Polosukhin, I · 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.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
Cited alongside, same era.
Synthetic and natural noise both break neural machine translation
Belinkov, Y. and Bisk, Y · 2018
Cited alongside, same era.
Hotflip: White-box adversarial examples for text classification
Ebrahimi, J., Rao, A., Lowd, D., and Dou, D · 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
Generating natural language adversarial examples through probability weighted word saliency
Ren, S., Deng, Y., He, K., and Che, W · 2019
Later among the works it cites.
Causality for machine learning
Schölkopf, B · 2019
Later among the works it cites.
When causal intervention meets adversarial examples and image masking for deep neural networks
Yang, C.-H. H., Liu, Y.-C., Chen, P.-Y., Ma, X., and Tsai, Y.-C. J · 2019
Later among the works it cites.
Towards stable and efficient training of verifiably robust neural networks, 2019
Zhang, H., Chen, H., Xiao, C., Gowal, S., Stanforth, R., Li, B., Boning, D., and Hsieh, C.-J · 2019
Later among the works it cites.
Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., and Wichmann, F. A · 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.
Adversarial example generation with syntactically controlled paraphrase networks
Iyyer, M., Wieting, J., Gimpel, K., and Zettlemoyer, L · 2018
Cited alongside, same era.
Deep text classification can be fooled
Liang, B., Li, H., Su, M., Bian, P., Li, X., and Shi, W · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Cited alongside, same era.
Semantically equivalent adversarial rules for debugging nlp models
Ribeiro, M. T., Singh, S., and Guestrin, C · 2018
Cited alongside, same era.
Towards fast computation of certified robustness for relu networks
Weng, L., Zhang, H., Chen, H., Song, Z., Hsieh, C.-J., Daniel, L., Boning, D., and Dhillon, I · 2018
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Wong, E. and Kolter, Z · 2018
Cited alongside, same era.
Is bert really robust? natural language attack on text classification and entailment
Jin, D., Jin, Z., Zhou, J. T., and Szolovits, P · 2020
Later among the works it cites.
Representation learning via invariant causal mechanisms
Mitrovic, J., McWilliams, B., Walker, J., Buesing, L., and Blundell, C · 2020
Later among the works it cites.
Robustness verification for transformers, 2020
Shi, Z., Zhang, H., Chang, K.-W., Huang, M., and Hsieh, C.-J · 2020
Later among the works it cites.
Long-tailed classification by keeping the good and removing the bad momentum causal effect
Tang, K., Huang, J., and Zhang, H · 2020
Later among the works it cites.
Few-shot domain adaptation by causal mechanism transfer
Teshima, T., Sato, I., and Sugiyama, M · 2020
Later among the works it cites.
Safer: A structure-free approach for certified robustness to adversarial word substitutions
Ye, M., Gong, C., and Liu, Q · 2020
Later among the works it cites.
Interventional few-shot learning
Yue, Z., Zhang, H., Sun, Q., and Hua, X.-S · 2020
Later among the works it cites.
Word-level textual adversarial attacking as combinatorial optimization
Zang, Y., Qi, F., Yang, C., Liu, Z., Zhang, M., Liu, Q., and Sun, M · 2020
Later among the works it cites.
Macer: Attack-free and scalable robust training via maximizing certified radius
Zhai, R., Dan, C., He, D., Zhang, H., Gong, B., Ravikumar, P., Hsieh, C.-J., and Wang, L · 2020
Later among the works it cites.
A causal view on robustness of neural networks
Zhang, C., Zhang, K., and Li, Y · 2020
Later among the works it cites.
Hidden killer: Invisible textual backdoor attacks with syntactic trigger
Qi, F., Li, M., Chen, Y., Zhang, Z., Liu, Z., Wang, Y., and Sun, M · 2021
Later among the works it cites.
Counterfactual generative networks
Sauer, A. and Geiger, A · 2021
Later among the works it cites.
Toward causal representation learning
Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., and Bengio, Y · 2021
Later among the works it cites.
Adversarial visual robustness by causal intervention
Tang, K., Tao, M., and Zhang, H · 2021
Later among the works it cites.
Certified robustness to text adversarial attacks by randomized [mask], 2021
Zeng, J., Zheng, X., Xu, J., Li, L., Yuan, L., and Huang, X · 2021
Later among the works it cites.
Adversarial robustness through the lens of causality
Zhang, Y., Gong, M., Liu, T., Niu, G., Tian, X., Han, B., Schölkopf, B., and Zhang, K · 2021
Later among the works it cites.