Weihua Hu

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Jure LeskovecProfessor of Computer Science, Stanford UniversityPatvirtintas el. paštas cs.stanford.edu
Matthias FeyFounding Engineer @ kumo.aiPatvirtintas el. paštas tu-dortmund.de
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoPatvirtintas el. paštas k.u-tokyo.ac.jp
Stefanie JegelkaTUM and MITPatvirtintas el. paštas mit.edu
Keyulu XuMITPatvirtintas el. paštas mit.edu
Percy LiangAssociate Professor of Computer Science, Stanford UniversityPatvirtintas el. paštas cs.stanford.edu
Gang NiuSenior Research Scientist, RIKEN Center for Advanced Intelligence ProjectPatvirtintas el. paštas riken.jp
Marinka ZitnikAssistant Professor, Harvard UniversityPatvirtintas el. paštas hms.harvard.edu
Hongyu RenOpenAIPatvirtintas el. paštas openai.com
C. Lawrence ZitnickFAIR (Meta)Patvirtintas el. paštas fb.com
Yuxiao DongCS, Tsinghua UniversityPatvirtintas el. paštas tsinghua.edu.cn
Bo HanHKBU / RIKENPatvirtintas el. paštas comp.hkbu.edu.hk
Peter BattagliaResearch Scientist, DeepMindPatvirtintas el. paštas google.com
Rémi LamDeepMindPatvirtintas el. paštas deepmind.com
Alvaro Sanchez GonzalezGoogle DeepMindPatvirtintas el. paštas google.com
Takeru MiyatoUniversity of Tübingen, Preferred Networks, Inc.Patvirtintas el. paštas uni-tuebingen.de
NAKATA MahoRIKENPatvirtintas el. paštas riken.go.jp
Kexin HuangStanford UniversityPatvirtintas el. paštas stanford.edu
Kaidi CaoStanford UniversityPatvirtintas el. paštas cs.stanford.edu
Jan Eric LenssenMax Planck Institute for Informatics, Kumo.aiPatvirtintas el. paštas mpi-inf.mpg.de

Stebėti

Weihua Hu

Stanford

Patvirtintas el. paštas stanford.edu - Pagrindinis puslapis

Deep Learning


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
How Powerful are Graph Neural Networks? K Xu, W Hu, J Leskovec, S Jegelka International Conference on Learning Representations, 2019	9203	2019
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	2877	2020
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	2370	2018
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	1619	2020
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	1466	2021
Learning skillful medium-range global weather forecasting R Lam, A Sanchez-Gonzalez, M Willson, P Wirnsberger, M Fortunato, ... Science, 2023	764*	2023
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	567	2017
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	533	2021
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	423	2021
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings H Ren, W Hu, J Leskovec International Conference on Learning Representations, 2020	346	2020
Does Distributionally Robust Supervised Learning Give Robust Classifiers? W Hu, G Niu, I Sato, M Sugiyama International Conference on Machine Learning, 2018	322	2018
Learning from complementary labels T Ishida, G Niu, W Hu, M Sugiyama Advances in Neural Information Processing Systems, 2017	191	2017
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	128	2021
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	88	2020
Temporal graph benchmark for machine learning on temporal graphs S Huang, F Poursafaei, J Danovitch, M Fey, W Hu, E Rossi, J Leskovec, ... Advances in Neural Information Processing Systems 36, 2024	78	2024
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	71	2021
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	54	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	34	2017
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	19	2022
Relational deep learning: Graph representation learning on relational databases M Fey, W Hu, K Huang, JE Lenssen, R Ranjan, J Robinson, R Ying, J You, ... arXiv preprint arXiv:2312.04615, 2023	11*	2023

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