Zhenmei ShiPh.D.
Computer Sciences
Google Scholar | Github | LinkedIn | CV | OpenReview |
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I am a Ph.D. candidate in Computer Science at the University of Wisconsin-Madison advised by Yingyu Liang. I obtained my B.S. degree in Computer Science and Pure Mathematics Advanced, from the Hong Kong University of Science and Technology in 2019.
Currently, I am a Research Intern at Google Cloud AI in fall 2024, Sunnyvale, working with Sercan Arik. This summer, I was an AI Research Scientist Intern at Salesforce, Palo Alto, working with Shafiq Joty. Recently, I was a Research Scientist Intern at Adobe, Seattle, working with Zhao Song.
My research interest mainly focuses on Understanding the learning and adaptation of Foundation Models, including Large Language Models, Vision Language Models, Diffusion Models, Shallow Networks, and so on. Feel free to contact me for collaboration.
I am actively seeking a Research Scientist position in the Great Bay or Great Seattle areas. Email me if you are interested in me.
Discovering the Gems in Early Layers: Accelerating Long-Context LLMs with 1000x Input Token Reduction
Zhenmei Shi, Yifei Ming, Xuan-Phi Nguyen, Yingyu Liang, Shafiq Joty arXiv, 2024 [ arXiv ] [ Code ] |
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Differential Privacy Mechanisms in Neural Tangent Kernel Regression
Jiuxiang Gu*, Yingyu Liang*, Zhizhou Sha*, Zhenmei Shi*, Zhao Song* WACV 2025 [ arXiv ] |
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Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models
Jiayu Wang, Yifei Ming, Zhenmei Shi, Vibhav Vineet, Xin Wang, Yixuan Li, Neel Joshi NeurIPS 2024 [ OpenReview ] [ arXiv ] [ Code ] [ Dataset ] |
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Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time
Yingyu Liang*, Zhizhou Sha*, Zhenmei Shi*, Zhao Song*, Yufa Zhou* NeurIPS 2024 Workshop [ OpenReview ] [ arXiv ] |
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Tensor Attention Training: Provably Efficient Learning of Higher-order Transformers
Yingyu Liang*, Zhenmei Shi*, Zhao Song*, Yufa Zhou* NeurIPS 2024 Workshop [ OpenReview ] [ arXiv ] |
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A Tighter Complexity Analysis of SparseGPT
Xiaoyu Li*, Yingyu Liang*, Zhenmei Shi*, Zhao Song* NeurIPS 2024 Workshop [ OpenReview ] [ arXiv ] |
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Differential Privacy of Cross-Attention with Provable Guarantee
Yingyu Liang*, Zhenmei Shi*, Zhao Song*, Yufa Zhou* NeurIPS 2024 Workshop [ OpenReview ] [ arXiv ] |
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Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability
Zhuoyan Xu*, Zhenmei Shi*, Yingyu Liang COLM 2024 [ OpenReview ] [ arXiv ] [ Workshop ] [ Code ] [ Slides ] [ Poster ] |
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Why Larger Language Models Do In-context Learning Differently?
Zhenmei Shi, Junyi Wei, Zhuoyan Xu, Yingyu Liang ICML 2024 [ Openreview ] [ arXiv ] [ Poster ] [ Workshop ] [ Workshop Poster ] |
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Fourier Circuits in Neural Networks and Transformers: A Case Study of Modular Arithmetic with Multiple Inputs
Chenyang Li*, Yingyu Liang*, Zhenmei Shi*, Zhao Song*, Tianyi Zhou* ICLR 2024 Workshop [ OpenReview ] [ arXiv ] [ Poster ] |
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Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning
Zhuoyan Xu, Zhenmei Shi, Junyi Wei, Fangzhou Mu, Yin Li, Yingyu Liang ICLR 2024 [ OpenReview ] [ arXiv ] [ Code ] [ Slides ] [ Poster ] [ Video ] [ Workshop ] [ Workshop Poster ] [ Workshop Slides ] |
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Domain Generalization via Nuclear Norm Regularization
Zhenmei Shi*, Yifei Ming*, Ying Fan*, Frederic Sala, Yingyu Liang CPAL 2024 Oral [ OpenReview ] [ arXiv ] [ Poster ] [ Code ] [ Slides ] [ Workshop ] [ Workshop Poster ] |
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Provable Guarantees for Neural Networks via Gradient Feature Learning
Zhenmei Shi*, Junyi Wei*, Yingyu Liang NeurIPS 2023 [ OpenReview ] [ arXiv ] [ Video ] [ Slides ] [ Poster ] |
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A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
Yiyou Sun, Zhenmei Shi, Yixuan Li NeurIPS 2023 Spotlight [ OpenReview ] [ arXiv ] [ Video ] [ Code ] [ Slides ] |
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When and How Does Known Class Help Discover Unknown Ones? Provable Understandings Through
Spectral Analysis
Yiyou Sun, Zhenmei Shi, Yingyu Liang, Yixuan Li ICML 2023 [ OpenReview ] [ arXiv ] [ Video ] [ Code ] |
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The Trade-off between Universality and Label Efficiency of Representations from Contrastive
Learning
Zhenmei Shi*, Jiefeng Chen*, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, Somesh Jha ICLR 2023 Spotlight (Accept Rate: 7.95%) [ OpenReview ] [ arXiv ] [ Poster ] [ Code ] [ Slides ] [ Video ] [ Workshop ] [ Workshop Poster ] |
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A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and
Advantage over Fixed
Features
Zhenmei Shi*, Junyi Wei*, Yingyu Liang ICLR 2022 [ OpenReview ] [ arXiv ] [ Poster ] [ Code ] [ Slides ] [ Video ] |
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Attentive Walk-Aggregating Graph Neural Networks
Mehmet F. Demirel, Shengchao Liu, Siddhant Garg, Zhenmei Shi, Yingyu Liang TMLR 2022 [ OpenReview ] [ arXiv ] [ Code ] |
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Deep Online Fused Video Stabilization
Zhenmei Shi, Fuhao Shi, Wei-Sheng Lai, Chia-Kai Liang, Yingyu Liang WACV 2022 [ Paper ] [ arXiv ] [ Poster ] [ Project ] [ Code ] [ Dataset ] |
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Structured Feature Learning for End-to-End Continuous Sign Language Recognition Zhaoyang Yang*, Zhenmei Shi*, Xiaoyong Shen, Yu-Wing Tai arXiv, 2019 [ arXiv ] [ News ] |
Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent
Bo Chen*, Xiaoyu Li*, Yingyu Liang*, Zhenmei Shi*, Zhao Song* arXiv, 2024 [ arXiv ] |
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Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix
Yingyu Liang*, Jiangxuan Long*, Zhenmei Shi*, Zhao Song*, Yufa Zhou* arXiv, 2024 [ arXiv ] |
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Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study
Yekun Ke*, Xiaoyu Li*, Yingyu Liang*, Zhenmei Shi*, Zhao Song* arXiv, 2024 [ arXiv ] |
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Looped ReLU MLPs May Be All You Need as Practical Programmable Computers
Yingyu Liang*, Zhizhou Sha*, Zhenmei Shi*, Zhao Song*, Yufa Zhou* arXiv, 2024 [ arXiv ] |
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Fine-grained Attention I/O Complexity: Comprehensive Analysis for Backward Passes
Xiaoyu Li*, Yingyu Liang*, Zhenmei Shi*, Zhao Song*, Yufa Zhou* arXiv, 2024 [ arXiv ] |
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HSR-Enhanced Sparse Attention Acceleration
Bo Chen*, Yingyu Liang*, Zhizhou Sha*, Zhenmei Shi*, Zhao Song* arXiv, 2024 [ arXiv ] |
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Fast John Ellipsoid Computation with Differential Privacy Optimization
Jiuxiang Gu*, Xiaoyu Li*, Yingyu Liang*, Zhenmei Shi*, Zhao Song*, Junwei Yu* arXiv, 2024 [ arXiv ] |
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Toward Infinite-Long Prefix in Transformer
Yingyu Liang*, Zhenmei Shi*, Zhao Song*, Chiwun Yang* arXiv, 2024 [ arXiv ] [ Code ] |
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Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspective
Yingyu Liang*, Zhenmei Shi*, Zhao Song*, Yufa Zhou* arXiv, 2024 [ arXiv ] |
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Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers
Yingyu Liang*, Heshan Liu*, Zhenmei Shi*, Zhao Song*, Zhuoyan Xu*, Junze Yin* arXiv, 2024 [ arXiv ] |
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Exploring the Frontiers of Softmax: Provable Optimization, Applications in Diffusion Model, and Beyond
Jiuxiang Gu*, Chenyang Li*, Yingyu Liang*, Zhenmei Shi*, Zhao Song* arXiv, 2024 [ arXiv ] |
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Dual Augmented Memory Network for Unsupervised Video Object Tracking
Zhenmei Shi*, Haoyang Fang*, Yu-Wing Tai, Chi Keung Tang arXiv, 2019 [ arXiv ] [ Project ] |
Velocity Vector Preserving Trajectory Simplification
Technical Report, 2018
[ Paper ] [ Code ] |
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Deep Colorization, 2018.
[ News ] |
Research Assistant
University of Wisconsin-Madison 2019 - Now | Yingyu Liang |
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Research Intern
Google Cloud AI in Sunnyvale, CA Fall 2024 | Sercan Arik |
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AI Research Scientist Intern
Salesforce in Palo Alto, CA Summer 2024 | Shafiq Joty |
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Research Scientist Intern
Adobe in Seattle, WA Spring 2024 | Zhao Song |
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Software Engineering Intern
Google in Mountain View, CA Summer 2021 | Myra Nam Summer 2020 | Fuhao Shi |
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Research Intern
Megvii (Face++) in Beijing Summer 2019 | Xiangyu Zhang |
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Research Intern
Tencent YouTu in Shenzhen Winter 2019 | Zhaoyang Yang and Yu-Wing Tai Winter 2018 | Xin Tao and Yu-Wing Tai |
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Research Assistant
Hong Kong University of Science and Technology 2018 - 2019 | Chi Keung Tang 2017 - 2018 | Raymond Wong Summer 2016 | Ji-Shan Hu |
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Research Intern
Oak Ridge National Laboratory in the USA Summer 2017 | Cheng Liu and Kwai L. Wong |