Fetching the paper…
Reading the bibliography…
Recent efforts show that neural networks are vulnerable to small but intentional perturbations on input features in visual classification tasks.
L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: Bringing order to the web.” Stanford InfoLab, Tech. Rep., 1999
1999
Earlier work this paper cites.
X. Zhu, Z. Ghahramani, and J. D. Lafferty, “Semi-supervised learning using gaussian fields and harmonic functions,” in Proceedings of the 20th International conference on Machine learning (ICML-03) , 2003, pp. 912–919
2003
Earlier work this paper cites.
D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf, “Learning with local and global consistency,” in Advances in neural information processing systems , 2004, pp. 321–328
2004
Earlier work this paper cites.
M. Belkin, P. Niyogi, and V. Sindhwani, “Manifold regularization: A geometric framework for learning from labeled and unlabeled examples,” Journal of machine learning research , pp. 2399–2434, 2006
2006
Earlier work this paper cites.
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, “Collective classification in network data,” AI magazine , vol. 29, no. 3, p. 93, 2008
2008
Earlier work this paper cites.
P. P. Talukdar and K. Crammer, “New regularized algorithms for transductive learning,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2009, pp. 442–457
2009
Earlier work this paper cites.
J. M. Joyce, “Kullback-leibler divergence,” International encyclopedia of statistical science , pp. 720–722, 2011
2011
Earlier work this paper cites.
J. Weston, F. Ratle, H. Mobahi, and R. Collobert, “Deep learning via semi-supervised embedding,” in Neural Networks: Tricks of the Trade . Springer, 2012, pp. 639–655
2012
Earlier work this paper cites.
B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 2014, pp. 701–710
2014
Earlier work this paper cites.
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun, “Spectral networks and locally connected networks on graphs,” ICLR , 2014
2014
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” ICLR , 2014
2014
Earlier work this paper cites.
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “Line: Large-scale information network embedding,” in Proceedings of the 24th International Conference on World Wide Web . International World Wide Web Conferences Steering Committee, 2015, pp. 1067–1077
2015
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” ICLR , 2015
2015
Earlier work this paper cites.
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, “Convolutional networks on graphs for learning molecular fingerprints,” in Advances in neural information processing systems , 2015, pp. 2224–2232
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
D. Wang, P. Cui, and W. Zhu, “Structural deep network embedding,” in SIGKDD . ACM, 2016, pp. 1225–1234
2016
Earlier work this paper cites.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in SIGKDD . ACM, 2016, pp. 855–864
2016
Earlier work this paper cites.
Z. Yang, W. Cohen, and R. Salakhudinov, “Revisiting semi-supervised learning with graph embeddings,” in International Conference on Machine Learning , 2016, pp. 40–48
2016
Earlier work this paper cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Advances in Neural Information Processing Systems , 2016, pp. 3844–3852
2016
Cited alongside, same era.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Advances in Neural Information Processing Systems , 2017, pp. 1024–1034
2017
Cited alongside, same era.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” ICLR , 2017
2017
Cited alongside, same era.
A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,” ICLR , 2017
2017
Cited alongside, same era.
T. Miyato, A. M. Dai, and I. Goodfellow, “Adversarial training methods for semi-supervised text classification,” ICLR , 2017
2017
F. Liao, M. Liang, Y. Dong, and T. Pang, “Defense against adversarial attacks using high-level representation guided denoiser,” CVPR , 2018
2018
Later among the works it cites.
F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel, “Ensemble adversarial training: Attacks and defenses,” ICLR , 2018
2018
Later among the works it cites.
M. Ding, J. Tang, and J. Zhang, “Semi-supervised learning on graphs with generative adversarial nets,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management . ACM, 2018, pp. 913–922
2018
Later among the works it cites.
W. Yu, C. Zheng, W. Cheng, C. C. Aggarwal, D. Song, B. Zong, H. Chen, and W. Wang, “Learning deep network representations with adversarially regularized autoencoders,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 2018, pp. 2663–2671
2018
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.
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard, “Universal adversarial perturbations,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017
2017
Cited alongside, same era.
Y. Wu, D. Bamman, and S. Russell, “Adversarial training for relation extraction,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing , 2017, pp. 1778–1783
2017
Cited alongside, same era.
H. Wang, J. Wang, J. Wang, M. Zhao, W. Zhang, F. Zhang, X. Xie, and M. Guo, “Graphgan: Graph representation learning with generative adversarial nets,” AAAI , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Z. Ying, J. You, C. Morris, X. Ren, W. Hamilton, and J. Leskovec, “Hierarchical graph representation learning with differentiable pooling,” in Advances in Neural Information Processing Systems , 2018, pp. 4800–4810
2018
Cited alongside, same era.
J. Ni, S. Chang, X. Liu, W. Cheng, H. Chen, D. Xu, and X. Zhang, “Co-regularized deep multi-network embedding,” in Proceedings of the 2018 World Wide Web Conference on World Wide Web . International World Wide Web Conferences Steering Committee, 2018, pp. 469–478
2018
Cited alongside, same era.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” ICLR , vol. 1, no. 2, 2018
2018
Cited alongside, same era.
S. Pan, R. Hu, G. Long, J. Jiang, L. Yao, and C. Zhang, “Adversarially regularized graph autoencoder for graph embedding.” in IJCAI , 2018, pp. 2609–2615
2018
Later among the works it cites.
Q. Dai, Q. Li, J. Tang, and D. Wang, “Adversarial network embedding,” AAAI , 2018
2018
Later among the works it cites.
X. He, Z. He, X. Du, and T.-S. Chua, “Adversarial personalized ranking for recommendation,” in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . ACM, 2018, pp. 355–364
2018
Later among the works it cites.
H. Gao, Z. Wang, and S. Ji, “Large-scale learnable graph convolutional networks,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 2018, pp. 1416–1424
2018
Later among the works it cites.
P. Cui, X. Wang, J. Pei, and W. Zhu, “A survey on network embedding,” IEEE Transactions on Knowledge and Data Engineering , vol. 31, no. 5, pp. 833–852, 2018
2018
Later among the works it cites.
D. Zhu, Z. Zhang, P. Cui, and W. Zhu, “Robust graph convolutional networks against adversarial attacks,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 2019, pp. 1399–1407
2019
Closest in time.
F. Feng, H. Chen, X. He, J. Ding, M. Sun, and T.-S. Chua, “Enhancing stock movement prediction with adversarial training,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence , 2019, pp. 5843–5849
2019
Closest in time.
Y. Liu, Z. Li, C. Zhou, Y. Jiang, J. Sun, M. Wang, and X. He, “Generative adversarial active learning for unsupervised outlier detection,” IEEE Transactions on Knowledge and Data Engineering , 2019
2019
Closest in time.
P. Wang, Y. Fu, H. Xiong, and X. Li, “Adversarial substructured representation learning for mobile user profiling,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 2019, pp. 130–138
2019
Closest in time.
J. Tang, X. Du, X. He, F. Yuan, Q. Tian, and T.-S. Chua, “Adversarial training towards robust multimedia recommender system,” IEEE Transactions on Knowledge and Data Engineering , 2019
2019
Closest in time.
X. Wang, X. He, M. Wang, F. Feng, and T.-S. Chua, “Neural graph collaborative filtering,” in Proceedings of the 42Nd International ACM SIGIR Conference on Research and Development in Information Retrieval . ACM, 2019, pp. 165–174
2019
Closest in time.
A. Raghunathan, J. Steinhardt, and P. Liang, “Certified defenses against adversarial examples,” ICLR , 2019
2019
Closest in time.
L. Sang, M. Xu, S. Qian, and X. Wu, “Aaane: Attention-based adversarial autoencoder for multi-scale network embedding,” in Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer, 2019, pp. 3–14
2019
Closest in time.