Unified Robust Training for Graph NeuralNetworks against Label Noise

Lu, Q., Getoor, L.: Link-based classification. In: ICML. pp. 496–503 (2003)

2003

Earlier work this paper cites.

Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-supervised learning using gaussian fields and harmonic functions. In: ICML. pp. 912–919 (2003)

2003

Earlier work this paper cites.

Sukhbaatar, S., Bruna, J., Paluri, M., Bourdev, L., Fergus, R.: Training convolutional networks with noisy labels. In: ICLR (2014)

2014

Earlier work this paper cites.

Goldberger, J., Ben-Reuven, E.: Training deep neural-networks using a noise adaptation layer. In: ICLR (2016)

2016

Earlier work this paper cites.

Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: ICML. pp. 1263–1272 (2017)

2017

Earlier work this paper cites.

Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: NeurIPS. pp. 1024–1034 (2017)

2017

Earlier work this paper cites.

Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)

2017

Earlier work this paper cites.

Malach, E., Shalev-Shwartz, S.: Decoupling “when to update” from “how to update”. In: NeurIPS. pp. 960–970 (2017)

2017

Earlier work this paper cites.

Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., Qu, L.: Making deep neural networks robust to label noise: A loss correction approach. In: CVPR. pp. 1944–1952 (2017)

2017

Cited alongside, same era.

Vahdat, A.: Toward robustness against label noise in training deep discriminative neural networks. In: NeurIPS. pp. 5596–5605 (2017)

2017

Cited alongside, same era.

Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., Sugiyama, M.: Co-teaching: Robust training of deep neural networks with extremely noisy labels. In: NeurIPS. pp. 8527–8537 (2018)

2018

Cited alongside, same era.

Jiang, L., Zhou, Z., Leung, T., Li, L.J., Fei-Fei, L.: MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels. In: ICML. pp. 2304–2313 (2018)

2018

Cited alongside, same era.

Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. In: AAAI. pp. 3538–3545 (2018)

Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. ICLR (2018)

2018

Later among the works it cites.

Zhang, Z., Sabuncu, M.: Generalized cross entropy loss for training deep neural networks with noisy labels. In: NeurIPS. pp. 8778–8788 (2018)

2018

Later among the works it cites.

Arazo, E., Ortego, D., Albert, P., O’Connor, N.E., McGuinness, K.: Unsupervised label noise modeling and loss correction. In: ICML. pp. 312–321 (2019)

2019

Later among the works it cites.

Han, J., Luo, P., Wang, X.: Deep self-learning from noisy labels. In: ICCV. pp. 5138–5147 (2019)

2019

Later among the works it cites.

NT, H., Jin, C., Murata, T.: Learning graph neural networks with noisy labels. In: 2nd ICLR Learning from Limited Labeled Data (LLD) Workshop (2019)

2019

Later among the works it cites.