Biography
I am an Assistant Professor at Qing Yuan Research Institute, Shanghai Jiao Tong University. I obtained the Ph.D. degree from Department of Computer Science and Technology, Tsinghua University in June 2022, under the supervision of Prof. Bo Zhang and Prof. Jun Zhu. Before that, I received the B.S. degree from Department of Computer Science and Technology, Tsinghua University in 2017. I was a visiting scholar from July 2016 to September 2016 in Machine Learning Department, Carnegie Mellon University, advised by Prof. Eric P. Xing.
My principal research interests lie in the development of machine learning and statistical methodology, spanning generative models, spectral methods, Bayesian inference, etc. The developed methods have been applied to scenarios such as image/language generation, semi-/self-/un-supervised learning, PDE solving, uncertainty quantification, and trustworthy ML.
Openings
I am looking for visitors and postdocs who have a strong background in mathematics/physics/computer science and are eager to get involved in cutting-edge, fundamental AI research (especially in fields of text, image, and multi-modal content generation). If you are interested, feel free to drop me an email.
Selected Publications [More]
* equal contribution. † corresponding author.
Efficient Large Language Models
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AdaMOE: Token-Adaptive Routing with Null Experts for Mixture-of-Experts Language Models
Zihao Zeng, Yibo Miao, Hongcheng Gao, Hao Zhang, and Zhijie Deng†
Findings of EMNLP, Miami, USA, 2024
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Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model
Yibo Miao, Hongcheng Gao, Hao Zhang†, and Zhijie Deng†
Findings of ACL, Bangkok, Thailand, 2024
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CLLMs: Consistency Large Language Models
Siqi Kou, Lanxiang Hu, Zhezhi He, Zhijie Deng†, and Hao Zhang
MLSys Young Professionals Symposium, Santa Clara, USA, 2024
International Conference on Machine Learning (ICML), Vienna, Austria, 2024
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Online Speculative Decoding
Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Ion Stoica, Zhijie Deng†, Alvin Cheung, and Hao Zhang†
International Conference on Machine Learning (ICML), Vienna, Austria, 2024
Deep Generative Models and Diffusion Models
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LOVECon: Text-driven Training-Free Long Video Editing with ControlNet
Zhenyi Liao and Zhijie Deng†
AI for Content Creation Workshop @ CVPR 2024, Seattle, USA, 2024
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BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference
Siqi Kou, Lei Gan, Dequan Wang, Chongxuan Li†, and Zhijie Deng†
International Conference on Learning Representations (ICLR), Vienna, Austria, 2024
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On Calibrating Diffusion Probabilistic Models
Tianyu Pang†, Cheng Lu, Chao Du, Min Lin, Shuicheng Yan, and Zhijie Deng†
Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023
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Structured Generative Adversarial Networks
Zhijie Deng*, Hao Zhang*, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, and Eric P Xing
Advances in Neural Information Processing Systems (NeurIPS), Long Beach, USA, 2017 (NVAIL Pioneering Research Award, 2017.12)
[ArXiv]
[code]
Date-efficient Learning with Neural Eigenfunctions
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Neural Eigenfunctions Are Structured Representation Learners
Zhijie Deng*, Jiaxin Shi*, Hao Zhang, Peng Cui, Cewu Lu, and Jun Zhu
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Improved Operator Learning by Orthogonal Attention
Zipeng Xiao, Zhongkai Hao, Bokai Lin, Zhijie Deng†, and Hang Su†
International Conference on Machine Learning (ICML), Vienna, Austria, 2024 (Spotlight)
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Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation
Zhijie Deng and Yucen Luo
International Conference on Computer Vision (ICCV), Paris, France, 2023
[ArXiv]
[code]
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NeuralEF: Deconstructing Kernels by Deep Neural Networks
Zhijie Deng, Jiaxin Shi, and Jun Zhu
International Conference on Machine Learning (ICML), Baltimore, USA, 2022
[ArXiv]
[code]
[slides]
Bayesian Deep Learning and Uncertainty Estimation
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Bayesian Exploration of Pre-trained Models for Low-shot Image Classification
Yibo Miao, Yu Lei, Feng Zhou†, and Zhijie Deng†
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 2024
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Towards Accelerated Model Training via Bayesian Data Selection
Zhijie Deng*, Peng Cui*, and Jun Zhu
Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023
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Accelerated Linearized Laplace Approximation for Bayesian Deep Learning
Zhijie Deng, Feng Zhou, and Jun Zhu
Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022 (Spotlight)
[ArXiv]
[code]
[slides]
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LiBRe: A Practical Bayesian Approach to Adversarial Detection
Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, and Jun Zhu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), VIRTUAL, 2021 (Valse 2021 Spotlight, 2021.10)
[code]
[slides]
Talks
- AIGC大模型的高效推理方法, 智源大会生成模型论坛, 2024.06
- 扩散模型:方法与应用, CSIG2023青年科学家会议扩散模型与内容生成论坛, 2023.12
- Online Speculative Decoding,安徽省安全人工智能研究院, 2023.12
- Deep Spectral Methods: Another Way to Unsupervised Learning, Bosch Center for AI (Shanghai), Shanghai Artificial Intelligence Laboratory, Kuaishou, SJTU, HFUT, and NUDT, 2023
- Bayesian Deep Learning: Methods and Applications, PRCV 2021, 2021.12
Selected Honors & Awards
- Outstanding Graduates, Department of Computer Science and Technology, Tsinghua University, 2022.06
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Huawei-Tsinghua Outstanding Service Award, 2021.06
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'84' Future Innovation Scholarship, 2020.12
- Tsinghua Good Graduates, Tsinghua University, 2017.06
- Outstanding Graduates, Department of Computer Science and Technology, Tsinghua University, 2017.06
Services
Conference reviewer:
NeurIPS 2019-; ICML 2021-; ICLR 2022-; CVPR 2021-; ICCV 2021-; AISTATS 2024-; ECCV 2022-; AAAI 2021-2022; AutoML 2022
Journal reviewer:
TPAMI,
TIP,
TNNLS,
Artificial Intelligence ,
TITS ,
Neural Networks ,
TMLR
Teaching
2024 spring, [CS7352] Advanced Neural Network Theory and Application, in cooperation with Prof. Weiran Huang and Prof. Yonglu Li, Shanghai Jiao Tong University
2024 spring, [CS3507] Science and Technology Innovation (Part 4-J), Shanghai Jiao Tong University
2023 fall, [AI1601] Artificial Intelligence Thinking and Ethics, in cooperation with Prof. Leilei Gu and Prof. Ge Zheng, Shanghai Jiao Tong University
2023 spring, [CS7310H] Algorithm Design and Analysis, in cooperation with Prof. Zhilei Xu, Shanghai Jiao Tong University
2022 fall, assistant of Deep Learning and the Application by Prof. Junchi Yan, Shanghai Jiao Tong University
2019 spring, TA in Statistical Machine Learning, instructed by Prof. Jun Zhu, Tsinghua University
2018 fall, TA in Machine Learning, instructed by Prof. Jie Tang and Prof. Jun Zhu, Tsinghua University
© 2024.09 Zhijie Deng