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Weihua Hu
Weihua Hu
Ph.D. student, Stanford University
Zweryfikowany adres z stanford.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
How Powerful are Graph Neural Networks?
K Xu*, W Hu*, J Leskovec, S Jegelka
International Conference on Learning Representations, 2019
31872019
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
9922018
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
Advances in Neural Information Processing Systems, 2020
7472020
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
4642020
Wilds: A benchmark of in-the-wild distribution shifts
PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, ...
International Conference on Machine Learning, 5637-5664, 2021
3462021
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
3092017
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
W Hu, G Niu, I Sato, M Sugiyama
International Conference on Machine Learning, 2018
1552018
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
H Ren*, W Hu*, J Leskovec
International Conference on Learning Representations, 2020
1082020
Learning from complementary labels
T Ishida, G Niu, W Hu, M Sugiyama
Advances in Neural Information Processing Systems, 2017
892017
Open catalyst 2020 (OC20) dataset and community challenges
L Chanussot, A Das, S Goyal, T Lavril, M Shuaibi, M Riviere, K Tran, ...
ACS Catalysis 11 (10), 6059-6072, 2021
842021
OGB-LSC: A large-scale challenge for machine learning on graphs
W Hu, M Fey, H Ren, M Nakata, Y Dong, J Leskovec
Advances in Neural Information Processing Systems, 2021
722021
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
402016
An introduction to electrocatalyst design using machine learning for renewable energy storage
CL Zitnick, L Chanussot, A Das, S Goyal, J Heras-Domingo, C Ho, W Hu, ...
arXiv preprint arXiv:2010.09435, 2020
222020
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
182017
Extending the wilds benchmark for unsupervised adaptation
S Sagawa, PW Koh, T Lee, I Gao, SM Xie, K Shen, A Kumar, W Hu, ...
International Conference on Learning Representations, 2021
152021
Forcenet: A graph neural network for large-scale quantum calculations
W Hu, M Shuaibi, A Das, S Goyal, A Sriram, J Leskovec, D Parikh, ...
arXiv preprint arXiv:2103.01436, 2021
152021
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
12019
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
Learning Backward Compatible Embeddings
W Hu, R Bansal, K Cao, N Rao, K Subbian, J Leskovec
ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022
2022
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