Di He - Concepedia

Author

Di He

Microsoft Research (United Kingdom)ORCID

Also Known As

Di HE, Di He, HE Di, He, Di, Wang, Liwei

Publications

1.9K

Citations

H-Index

Concepts

All Affiliations

Microsoft Research (United Kingdom)Peking University University of Illinois Urbana-Champaign Microsoft Research Asia (China)Tsinghua University

About

Di He is an author at Microsoft Research (United Kingdom) specializing in engineering, computer science, and machine learning.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	16	19	1.5K
2	Computer Science	13	13	1K
3	Language Studies	6	6	198
4	Computer Engineering	6	6	578
5	Civil Engineering	1	1	14

Di He

Publications

1.7K

Citations

H-Index

Page 1

	Year	Citations
Do Transformers Really Perform Badly for Graph Representation Chengxuan Ying, Tianle Cai, Shengjie Luo, Neural Information Processing Systems EngineeringGraph TheoryData SciencePerform BadlyNetwork Analysis	2021	384
On Layer Normalization in the Transformer Architecture Ruibin Xiong, Yunchang Yang, Di He, Llm Fine-tuningLayer Normalization MattersEngineeringMachine LearningLayer Normalization	2020	260
Do Transformers Really Perform Bad for Graph Representation? Chengxuan Ying, Tianle Cai, Shengjie Luo, arXiv (Cornell University) Natural Language ProcessingGeometric LearningStructural InformationGraph Representation LearningGraph Theory	2021	125
Effect of Mineral Dissolution/Precipitation and CO<sub>2</sub> Exsolution on CO<sub>2</sub> transport in Geological Carbon Storage Ruina Xu, Rong Li, Jin Ma, Accounts of Chemical Research	2017	122
Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View Yiping Lu, Zhuohan Li, Di He, arXiv (Cornell University) Natural Language ProcessingNumerical AnalysisComputational ScienceEngineeringMultiple Particles	2019	115
On Layer Normalization in the Transformer Architecture Ruibin Xiong, Yunchang Yang, Di He, arXiv (Cornell University) Llm Fine-tuningLayer Normalization MattersEngineeringMachine LearningLayer Normalization	2020	108
Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai, Tianle Cai, Di He, arXiv (Cornell University) Artificial IntelligenceNeural Network RobustnessEngineeringMachine LearningData Science	2019	90
Pore-Scale Experimental Investigation of the Effect of Supercritical CO<sub>2</sub> Injection Rate and Surface Wettability on Salt Precipitation Di He, Peixue Jiang, Ruina Xu Environmental Science & Technology	2019	79
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training Tianle Cai, Shengjie Luo, Keyulu Xu, arXiv (Cornell University) Geometric LearningDeep Neural NetworksGraph Neural NetworksGraph TheoryMachine Learning	2020	74
Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter Lijun Wu, Xu Tan, Di He, Beyond Error PropagationEngineeringNeurolinguisticsLanguage LearningCorpus Linguistics	2018	59

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