Weihua Hu
Weihua Hu
Ph.D. student, Stanford University
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
How Powerful are Graph Neural Networks?
K Xu*, W Hu*, J Leskovec, S Jegelka
International Conference on Learning Representations, 2019
6732019
Co-teaching: robust training deep neural networks with extremely noisy labels
B Han, Q Yao, X Yu, G Niu, M Xu, W Hu, I Tsang, M Sugiyama
Advances in Neural Information Processing Systems, 2018
2312018
Learning Discrete Representations via Information Maximizing Self-Augmented Training
W Hu, T Miyato, S Tokui, E Matsumoto, M Sugiyama
International Conference on Machine Learning, 2017
1292017
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
W Hu, G Niu, I Sato, M Sugiyama
International Conference on Machine Learning, 2018
432018
Strategies for Pre-training Graph Neural Networks
W Hu*, B Liu*, J Gomes, M Zitnik, P Liang, V Pande, J Leskovec
International Conference on Learning Representations, 2020
422020
Learning from complementary labels
T Ishida, G Niu, W Hu, M Sugiyama
Advances in neural information processing systems, 5639-5649, 2017
312017
A Latent Concept Topic Model for Robust Topic Inference Using Word Embeddings
W Hu, J Tsujii
The Annual Meeting of the Association for Computational Linguistics, 380-386, 2016
272016
Open Graph Benchmark: Datasets for Machine Learning on Graphs
W Hu, M Fey, M Zitnik, Y Dong, H Ren, B Liu, M Catasta, J Leskovec
arXiv preprint arXiv:2005.00687, 2020
252020
Worst-case redundancy of optimal binary AIFV codes and their extended codes
W Hu, H Yamamoto, J Honda
IEEE Transactions on Information Theory 63 (8), 5074-5086, 2017
132017
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
H Ren*, W Hu*, J Leskovec
International Conference on Learning Representations, 2020
82020
Tight upper bounds on the redundancy of optimal binary AIFV codes
W Hu, H Yamamoto, J Honda
IEEE International Symposium on Information Theory, 6-10, 2016
12016
Unsupervised Discrete Representation Learning
W Hu, T Miyato, S Tokui, E Matsumoto, M Sugiyama
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 97-119, 2019
2019
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Articles 1–12