Weihua Hu - Google Scholar Citations

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	All	Since 2015
Citations	565	565
h-index	7	7
i10-index	7	7

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201720182019202012 72 403 75

Co-authors

Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
Gang NiuRIKEN Center for Advanced Intelligence ProjectVerified email at postman.riken.jp
Jure LeskovecProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Stefanie JegelkaMITVerified email at mit.edu
Keyulu XuMITVerified email at mit.edu
Takeru MiyatoPreferred Networks, Inc.Verified email at preferred.jp
Bo HanHKBU / RIKENVerified email at comp.hkbu.edu.hk
Hirosuke YamamotoThe University of Tokyo (Professor Emeritus)Verified email at ieee.org
Junya HondaThe University of Tokyo / RIKENVerified email at stat.t.u-tokyo.ac.jp
Takashi IshidaPh.D. student, The University of TokyoVerified email at ms.k.u-tokyo.ac.jp
Junichi TsujiiArtificial Intelligence Research Center, AISTVerified email at aist.go.jp
Percy LiangAssociate Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Vijay PandeProfessor of Chemistry, Stanford UniversityVerified email at stanford.edu
Marinka ZitnikAssistant Professor, Harvard UniversityVerified email at hms.harvard.edu

Weihua Hu

Ph.D. student, Stanford University

Verified email at stanford.edu - Homepage

Machine Learning Deep Learning Drug Discovery


Title	Cited by	Year
How Powerful are Graph Neural Networks? K Xu, W Hu, J Leskovec, S Jegelka International Conference on Learning Representations, 2019	300	2019
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	107	2018
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	79	2017
Does Distributionally Robust Supervised Learning Give Robust Classifiers? W Hu, G Niu, I Sato, M Sugiyama International Conference on Machine Learning, 2018	24	2018
Learning from complementary labels T Ishida, G Niu, W Hu, M Sugiyama Advances in neural information processing systems, 5639-5649, 2017	21	2017
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	19	2016
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	11	2017
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	3*	2020
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	1	2016
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings H Ren, W Hu, J Leskovec International Conference on Learning Representations, 2020		2020
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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