| CARVIEW |
Lei Huang (黄 雷)
Associate Professor
State Key Laboratory of Complex and Critical Software Environment (SKLCCSE)
(Previously SKLSDE)
School of Artificial Intelligence, Beihang University, Beijing, China
Office: Room B1009, New Main Building, Beihang University, Haidian, Beijing
Email: huangleiai@buaa.edu.cn
Email (Optional): huanglei36060520@gmail.com
[Publication] | [Professional Activities] | [Students] |[Teaching] |[Education] | [Awards and Honors] | [Source Code]| [Miscellaneous] | [CV]
Publications
I am addicted to understanding and debugging the training of DNNs. I believe one avenue is delving into the basic modules of DNNs, e.g., normalization layer and linear layer. My contributions for the community are mainly on designing and understanding these basic modules and their compound for the training dynamics of DNNs (* indicates corresponding authors; # indicates equal contributions).Book
Selected papers | All paper list
Normalization layer (algorithms and analyses):
![[Poster]](carview.php?tsp=./poster/CVPR2018_poster.png){kind=link}
Linear layer with constraints:
Understanding the representation and training of DNNs.:
Professional Activities
Conference Reviewer:NeurIPS2025, ICCV2025, CVPR2025, ICLR2025, AAAI2025, NeurIPS2024, ECCV2024, ICML 2024, CVPR2024, ICLR2024, AAAI2024, NeurIPS2023, ICCV2023, ICML2023, CVPR2023, ICLR2023, AAAI2023, NeurIPS2022, ECCV2022, ICML 2022, CVPR 2022, AAAI 2022, NeurIPS2021, ICCV2021, ICML 2021, CVPR 2021, AAAI 2021, WACV 2021, NeurIPS 2020, ACM Multimedia 2020, ECCV2020, IJCAI2020, CVPR2020, AAAI2020, ICCV 2019, CVPR 2019, AAAI 2019, ACM Multimedia 2019
Journal Reviewer: IEEE TPAMI, JMLR, TMLR (Transactions on Machine Learning Research), IJCV, IEEE Transactions on Cybernetics, TNNLS (IEEE Transactions on Neural Networks and Learning Systems), PR.
Sub-Reviewer of CVPR 2016, NIPS 2016, IJCAI 2017
Students
Graduate students officially: Xi Weng (2022-now), Junlong Jia (2022-now), Yunhao Ni (2023-now)， Xingjian Zhang (2024-now), Jianing An (2024-now)
Students supervised unofficially: Diwen Wan (Internship in IIAI of UAE between 2018-2020), Lei Zhao (Beihang University, 2019 - now), Jiaxi Wang (Tsinghua University, 2020 - 2022), Jiawei Zhang (Beihang University, 2021 - now), Ge Kan (Beihang University, 2021 - 2022), LuoChe Wang (2023-now)
Teaching
Introduction of Artificial Intelligence (人工智能通识课, B410026001); Beihang University, Spring of 2025
Machine Learning (B3I423170); Beihang University， Fall of 2021、2022、2023、2024
Deep Learning (B3J420160); Beihang University, Spring of 2022、2023、2024、2025
Computer Vision (B3J424120); Beihang University, Fall of 2021、2022、2023
Advanced Deep Learning (D411051007, for postgraduate); Beihang University, Spring of 2023、2024、2025
Reinforcement Learning (D411051008, for postgraduate); Beihang University, Spring of 2023、2024, and Fall of 2024
Generative AI and LLMs (T411041001, for postgraduate); Beihang University, Fall of 2024
Pattern recognition and machine Learning (42112110, for postgraduate); Beihang University, Fall of 2022
Project
国家科技创新2030-“新一代人工智能”重大项目（旗舰项目）课题，基础模型的鲁棒性评测机理、方法与工具研究，2022.12-2025.11，主持（课题负责人）
国家自然科学基金面上项目，深度神经网络机制可解释性研究，2025.01-2028.12，主持
国家自然科学基金青年项目，深度神经网络中激活值标准化技术研究，2022.01-2024.12，主持
国家科技计划项目，代码安全缺陷智能加固技术研究，2024.12-2025.11，主持
北京航空航天大学青年拔尖人才计划项目，深度神经网络模型设计与分析，2022.07-2027.06，主持
软件开发环境国家重点实验室探索性自选课题，分布可控的深度神经网络模型分析与设计，2021.01-2022.12，主持
复杂关键软件环境全国重点实验室探索性自选课题，面向无人群智系统的深度神经网络技术研究，2023.01-2024.12，主持
国家科技创新2030-“新一代人工智能”重大项目，基于人机协作的复杂智能软件系统构造与演 化技术，2021.12-2024.11，参与
国家自然科学基金“数据科学与人工智能前沿探索”专项项目，基于新型架构的行业大模型研究，2024.12-2026.11，参与
北京航空航天大学理工交叉融合“十大科学问题” 项目，群体智能涌现机理与运行机制，2022.07-2025.06，参与
Education
- Sep. 2015 - Oct. 2016
- Visiting Ph.D student in Vision & Learning Lab, at the University of Michigan, Ann Arbor
- Research advisor: Profs. Jia Deng
- Sep. 2011 - 2018
- Ph.D student in Computer Science, School of Computer Science and Engineering, Beihang University
- Advisor: Profs. Wei Li and co-advised by Profs. Bo Lang
- Sep. 2010 - Sep. 2011
- Master student in Computer Science, School of Computer Science and Engineering, Beihang University
- Advisor: Profs. Wei Li and co-advised by Profs. Bo Lang
- Sep. 2006 - Jun. 2010
- B.Sc in Computer Science, School of Computer Science and Engineering, Beihang University
- Thesis advisor: Profs. Wei Li
Talk Slides
September 8th, 2018. Normalization Methods for Training Deep Neural Networks: Theory and Practice, ECCV 2018 Tutorial. Munich, Germany. [Slides]
August 17th, 2017. Normalization techiniques in deep learning. Multimedia Signal and Intelligent Information Processing Laboratory, Tsinghua University, Beijing. [Slides]
November 2016- January 2017. Deep Learning Seminar For graduated students in State Key Laboratory of Software Development Environment, Beihang University, Beijing. [slides1-Introduction] [slides2-MLP] [slides3-CNN] [slides4-RNN]
November 1th, 2014. Graph-based active Semi-Supervised Learning: a new perspective for relieving multi-class annotation labor. ICT International Exchange Workshop 2014, Laboratory of Advanced Research B, University of Tsukuba, Japan [Slides]
Source Code
ONI: This project is the implementation of the paper "Controllable Orthogonalization in Training DNNs" (arXiv:2004.00917).
StochasticityBW: This project is the implementation of the paper "An Investigation into the Stochasticity of Batch Whitening" (arXiv:2003.12327).
IterNorm: This project is the implementation of the paper "Iterative Normalization: Beyond Standardization towards Efficient Whitening" (arXiv:1904.03441).
DBN: This project is the Torch implementation of the paper : Decorrelated Batch normalization (arXiv:1804.08450).
OWN: This project is the Torch implementation of the paper : orthogonal weight normalization method for solving orthogonality constraints over Steifel manifold in deep neural networks (arXiv:1709.06079).
CWN: This project is the Torch implementation of our accepted ICCV 2017 paper: Centered Weight Normalization in Accelerating Training of Deep Neural Networks
NormProjection: This project is the Torch implementation of the paper: Projection Based Weight Normalization for Deep Neural Networks (arXiv:1710.02338)
Ladder_deepSSL_NP: The reimplementation of Ladder networks with projection based weight normalization. We achieved test errors as 2.52%, 1.06%, and 0.91% on Permunate invariant MNIST dataset with 20, 50, and 100 labeled samples respectively, which is the state-of-the-art results.
Miscellaneous
- I also have great interest in hiphop dance (particularly in breakin dance), singing and guitar. you can find some videos of my show on my homepage of Meipai and YouTube
