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Mu Li 0003
Person information
- affiliation: Amazon, Palo Alto, CA, USA
- affiliation (former): Carnegie Mellon University, PA, USA
Other persons with the same name
- Mu Li — disambiguation page
- Mu Li 0001 — Microsoft Research Asia, Beijing, China
- Mu Li 0002 — Pennsylvania State University, University Park, PA, USA
- Mu Li 0004 — Beihang University, Beijing, China
- Mu Li 0005
— The Chinese University of Hong Kong, Shenzhen, China (and 3 more)
- Mu Li 0006
- Mu Li 0007
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2020 – today
- 2024
- [j7]Peng Zhou
Online learning from capricious data streams via shared and new feature spaces. Appl. Intell. 54(19): 9429-9445 (2024) - [j6]Yi Zhu
Improving Semantic Segmentation via Efficient Self-Training. IEEE Trans. Pattern Anal. Mach. Intell. 46(3): 1589-1602 (2024) - [j5]Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, Alex Smola:
Multimodal Chain-of-Thought Reasoning in Language Models. Trans. Mach. Learn. Res. 2024 (2024) - 2023
- [j4]Shengju Qian
What Makes for Good Tokenizers in Vision Transformer? IEEE Trans. Pattern Anal. Mach. Intell. 45(11): 13011-13023 (2023) - [c38]Yuxin Ren, Zihan Zhong, Xingjian Shi, Yi Zhu, Chun Yuan, Mu Li:
Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation. ACL (1) 2023: 1990-2006 - [c37]Jiaao Chen, Aston Zhang, Mu Li, Alex Smola, Diyi Yang:
A Cheaper and Better Diffusion Language Model with Soft-Masked Noise. EMNLP 2023: 4765-4775 - [c36]Zhuosheng Zhang, Aston Zhang, Mu Li, Alex Smola:
Automatic Chain of Thought Prompting in Large Language Models. ICLR 2023 - [c35]Jiaao Chen, Aston Zhang, Xingjian Shi, Mu Li, Alex Smola, Diyi Yang:
Parameter-Efficient Fine-Tuning Design Spaces. ICLR 2023 - [c34]Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson:
Learning Multimodal Data Augmentation in Feature Space. ICLR 2023 - [c33]Taojiannan Yang, Yi Zhu, Yusheng Xie, Aston Zhang, Chen Chen, Mu Li:
AIM: Adapting Image Models for Efficient Video Action Recognition. ICLR 2023 - [c32]Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, Mahsa Shoaran:
XTab: Cross-table Pretraining for Tabular Transformers. ICML 2023: 43181-43204 - [c31]Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Yi Zhu, Mu Li, Yuyang Wang:
PreDiff: Precipitation Nowcasting with Latent Diffusion Models. NeurIPS 2023 - [c30]Shuhuai Ren, Aston Zhang, Yi Zhu, Shuai Zhang, Shuai Zheng, Mu Li, Alexander J. Smola, Xu Sun:
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition. NeurIPS 2023 - [c29]Xiaoshuai Hao, Yi Zhu, Srikar Appalaraju, Aston Zhang, Wanqian Zhang, Bo Li, Mu Li:
MixGen: A New Multi-Modal Data Augmentation. WACV (Workshops) 2023: 379-389 - [i51]Jiaao Chen, Aston Zhang, Xingjian Shi, Mu Li, Alex Smola, Diyi Yang:
Parameter-Efficient Fine-Tuning Design Spaces. CoRR abs/2301.01821 (2023) - [i50]Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, Alex Smola:
Multimodal Chain-of-Thought Reasoning in Language Models. CoRR abs/2302.00923 (2023) - [i49]Taojiannan Yang, Yi Zhu, Yusheng Xie, Aston Zhang, Chen Chen, Mu Li:
AIM: Adapting Image Models for Efficient Video Action Recognition. CoRR abs/2302.03024 (2023) - [i48]Matías Mendieta, Boran Han, Xingjian Shi, Yi Zhu, Chen Chen, Mu Li:
GFM: Building Geospatial Foundation Models via Continual Pretraining. CoRR abs/2302.04476 (2023) - [i47]Jiaxin Cheng, Xiao Liang, Xingjian Shi, Tong He, Tianjun Xiao, Mu Li:
LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation. CoRR abs/2302.08908 (2023) - [i46]Cody Hao Yu, Haozheng Fan, Guangtai Huang, Zhen Jia, Yizhi Liu, Jie Wang, Zach Zheng, Yuan Zhou, Haichen Shen, Junru Shao, Mu Li, Yida Wang:
RAF: Holistic Compilation for Deep Learning Model Training. CoRR abs/2303.04759 (2023) - [i45]Shuhuai Ren, Aston Zhang, Yi Zhu, Shuai Zhang, Shuai Zheng, Mu Li, Alex Smola, Xu Sun:
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition. CoRR abs/2304.04704 (2023) - [i44]Jiaao Chen, Aston Zhang, Mu Li, Alex Smola, Diyi Yang:
A Cheaper and Better Diffusion Language Model with Soft-Masked Noise. CoRR abs/2304.04746 (2023) - [i43]Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, Mahsa Shoaran:
XTab: Cross-table Pretraining for Tabular Transformers. CoRR abs/2305.06090 (2023) - [i42]Yuxin Ren, Zihan Zhong, Xingjian Shi, Yi Zhu, Chun Yuan, Mu Li:
Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation. CoRR abs/2305.09651 (2023) - [i41]Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Yi Zhu, Mu Li, Yuyang Wang:
PreDiff: Precipitation Nowcasting with Latent Diffusion Models. CoRR abs/2307.10422 (2023) - 2022
- [j3]Zhen Zhang, Shuai Zheng, Yida Wang, Justin Chiu, George Karypis, Trishul Chilimbi, Mu Li, Xin Jin:
MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud. Proc. VLDB Endow. 16(1): 37-50 (2022) - [c28]Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Haibin Lin, Zhi Zhang, Yue Sun, Tong He, Jonas Mueller, R. Manmatha, Mu Li, Alexander J. Smola:
ResNeSt: Split-Attention Networks. CVPR Workshops 2022: 2735-2745 - [c27]Likun Cai, Zhi Zhang, Yi Zhu, Li Zhang, Mu Li, Xiangyang Xue:
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training. CVPR Workshops 2022: 4776-4786 - [c26]Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi, Mu Li, Zhangyang Wang:
Removing Batch Normalization Boosts Adversarial Training. ICML 2022: 23433-23445 - [c25]Haotao Wang, Aston Zhang, Yi Zhu, Shuai Zheng, Mu Li, Alex J. Smola, Zhangyang Wang:
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition. ICML 2022: 23446-23458 - [c24]Zhihan Gao, Xingjian Shi, Hao Wang, Yi Zhu, Yuyang Wang, Mu Li, Dit-Yan Yeung:
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting. NeurIPS 2022 - [i40]Likun Cai, Zhi Zhang, Yi Zhu, Li Zhang, Mu Li, Xiangyang Xue:
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training. CoRR abs/2203.13249 (2022) - [i39]Zhen Zhang, Shuai Zheng, Yida Wang, Justin Chiu, George Karypis, Trishul Chilimbi, Mu Li, Xin Jin:
MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud. CoRR abs/2205.00119 (2022) - [i38]Xiaoshuai Hao, Yi Zhu, Srikar Appalaraju, Aston Zhang, Wanqian Zhang, Bo Li, Mu Li:
MixGen: A New Multi-Modal Data Augmentation. CoRR abs/2206.08358 (2022) - [i37]Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi, Mu Li, Zhangyang Wang:
Removing Batch Normalization Boosts Adversarial Training. CoRR abs/2207.01156 (2022) - [i36]Haotao Wang, Aston Zhang, Yi Zhu, Shuai Zheng, Mu Li, Alex Smola, Zhangyang Wang:
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition. CoRR abs/2207.01160 (2022) - [i35]Zhihan Gao, Xingjian Shi, Hao Wang, Yi Zhu, Yuyang Wang, Mu Li, Dit-Yan Yeung:
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting. CoRR abs/2207.05833 (2022) - [i34]Zhuosheng Zhang, Aston Zhang, Mu Li, Alex Smola:
Automatic Chain of Thought Prompting in Large Language Models. CoRR abs/2210.03493 (2022) - [i33]Yunhe Gao, Xingjian Shi, Yi Zhu, Hao Wang, Zhiqiang Tang, Xiong Zhou, Mu Li, Dimitris N. Metaxas:
Visual Prompt Tuning for Test-time Domain Adaptation. CoRR abs/2210.04831 (2022) - [i32]Jielin Qiu, Yi Zhu, Xingjian Shi, Florian Wenzel, Zhiqiang Tang, Ding Zhao
Are Multimodal Models Robust to Image and Text Perturbations? CoRR abs/2212.08044 (2022) - [i31]M. Saiful Bari, Aston Zhang, Shuai Zheng, Xingjian Shi, Yi Zhu, Shafiq Joty, Mu Li:
SPT: Semi-Parametric Prompt Tuning for Multitask Prompted Learning. CoRR abs/2212.10929 (2022) - [i30]Shengju Qian, Yi Zhu, Wenbo Li
What Makes for Good Tokenizers in Vision Transformer? CoRR abs/2212.11115 (2022) - [i29]Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson:
Learning Multimodal Data Augmentation in Feature Space. CoRR abs/2212.14453 (2022) - 2021
- [c23]Cody Hao Yu, Xingjian Shi, Haichen Shen, Zhi Chen, Mu Li, Yida Wang:
Lorien: Efficient Deep Learning Workloads Delivery. SoCC 2021: 18-32 - [c22]Haoyu He, Xingjian Shi, Jonas Mueller, Sheng Zha, Mu Li, George Karypis:
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing. SustaiNLP@EMNLP 2021: 119-133 - [c21]Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris N. Metaxas:
CrossNorm and SelfNorm for Generalization under Distribution Shifts. ICCV 2021: 52-61 - [c20]Fangrui Zhu, Yi Zhu, Li Zhang, Chongruo Wu, Yanwei Fu
A Unified Efficient Pyramid Transformer for Semantic Segmentation. ICCVW 2021: 2667-2677 - [c19]Haofei Kuang, Yi Zhu, Zhi Zhang, Xinyu Li, Joseph Tighe, Sören Schwertfeger, Cyrill Stachniss
Video Contrastive Learning with Global Context. ICCVW 2021: 3188 - [c18]Haichen Shen, Jared Roesch, Zhi Chen, Wei Chen, Yong Wu, Mu Li, Vin Sharma, Zachary Tatlock, Yida Wang:
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference. MLSys 2021 - [c17]Shengju Qian, Hao Shao, Yi Zhu, Mu Li, Jiaya Jia:
Blending Anti-Aliasing into Vision Transformer. NeurIPS 2021: 5416-5429 - [c16]Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander J. Smola:
Benchmarking Multimodal AutoML for Tabular Data with Text Fields. NeurIPS Datasets and Benchmarks 2021 - [c15]Li Wang, Li Zhang, Yi Zhu, Zhi Zhang, Tong He, Mu Li, Xiangyang Xue:
Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS 2021: 13364-13377 - [i28]Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris N. Metaxas:
SelfNorm and CrossNorm for Out-of-Distribution Robustness. CoRR abs/2102.02811 (2021) - [i27]Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola:
Dive into Deep Learning. CoRR abs/2106.11342 (2021) - [i26]Fangrui Zhu, Yi Zhu, Li Zhang, Chongruo Wu, Yanwei Fu, Mu Li:
A Unified Efficient Pyramid Transformer for Semantic Segmentation. CoRR abs/2107.14209 (2021) - [i25]Haofei Kuang, Yi Zhu, Zhi Zhang, Xinyu Li, Joseph Tighe, Sören Schwertfeger, Cyrill Stachniss, Mu Li:
Video Contrastive Learning with Global Context. CoRR abs/2108.02722 (2021) - [i24]Li Wang, Li Zhang, Yi Zhu, Zhi Zhang, Tong He, Mu Li, Xiangyang Xue:
Progressive Coordinate Transforms for Monocular 3D Object Detection. CoRR abs/2108.05793 (2021) - [i23]Haoyu He, Xingjian Shi, Jonas Mueller, Sheng Zha, Mu Li, George Karypis:
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing. CoRR abs/2109.11105 (2021) - [i22]Shengju Qian, Hao Shao, Yi Zhu, Mu Li, Jiaya Jia:
Blending Anti-Aliasing into Vision Transformer. CoRR abs/2110.15156 (2021) - [i21]Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander J. Smola:
Benchmarking Multimodal AutoML for Tabular Data with Text Fields. CoRR abs/2111.02705 (2021) - 2020
- [j2]Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng, Yi Zhu:
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. J. Mach. Learn. Res. 21: 23:1-23:7 (2020) - [c14]Cong Xie, Shuai Zheng, Oluwasanmi Koyejo, Indranil Gupta, Mu Li, Haibin Lin:
CSER: Communication-efficient SGD with Error Reset. NeurIPS 2020 - [c13]Yuwei Hu, Zihao Ye
FeatGraph: a flexible and efficient backend for graph neural network systems. SC 2020: 71 - [i20]Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, Alexander J. Smola:
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. CoRR abs/2003.06505 (2020) - [i19]Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Mueller, R. Manmatha, Mu Li, Alexander J. Smola:
ResNeSt: Split-Attention Networks. CoRR abs/2004.08955 (2020) - [i18]Yi Zhu, Zhongyue Zhang, Chongruo Wu, Zhi Zhang, Tong He, Hang Zhang, R. Manmatha, Mu Li, Alexander J. Smola:
Improving Semantic Segmentation via Self-Training. CoRR abs/2004.14960 (2020) - [i17]Haichen Shen, Jared Roesch, Zhi Chen, Wei Chen, Yong Wu, Mu Li, Vin Sharma, Zachary Tatlock, Yida Wang:
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference. CoRR abs/2006.03031 (2020) - [i16]Shuai Zheng, Haibin Lin, Sheng Zha, Mu Li:
Accelerated Large Batch Optimization of BERT Pretraining in 54 minutes. CoRR abs/2006.13484 (2020) - [i15]Cong Xie, Shuai Zheng, Oluwasanmi Koyejo, Indranil Gupta, Mu Li, Haibin Lin:
CSER: Communication-efficient SGD with Error Reset. CoRR abs/2007.13221 (2020) - [i14]Yuwei Hu, Zihao Ye, Minjie Wang, Jiali Yu, Da Zheng, Mu Li, Zheng Zhang, Zhiru Zhang, Yida Wang:
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems. CoRR abs/2008.11359 (2020) - [i13]Yi Zhu, Xinyu Li, Chunhui Liu, Mohammadreza Zolfaghari, Yuanjun Xiong, Chongruo Wu, Zhi Zhang, Joseph Tighe, R. Manmatha, Mu Li:
A Comprehensive Study of Deep Video Action Recognition. CoRR abs/2012.06567 (2020)
2010 – 2019
- 2019
- [j1]Ye Yuan
On the Powerball Method: Variants of Descent Methods for Accelerated Optimization. IEEE Control. Syst. Lett. 3(3): 601-606 (2019) - [c12]Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, Mu Li:
Bag of Tricks for Image Classification with Convolutional Neural Networks. CVPR 2019: 558-567 - [c11]Leyuan Wang, Zhi Chen, Yizhi Liu, Yao Wang, Lianmin Zheng, Mu Li, Yida Wang:
A Unified Optimization Approach for CNN Model Inference on Integrated GPUs. ICPP 2019: 99:1-99:10 - [c10]Yizhi Liu, Yao Wang, Ruofei Yu, Mu Li, Vin Sharma, Yida Wang:
Optimizing CNN Model Inference on CPUs. USENIX ATC 2019: 1025-1040 - [i12]Zhi Zhang, Tong He, Hang Zhang, Zhongyue Zhang, Junyuan Xie, Mu Li:
Bag of Freebies for Training Object Detection Neural Networks. CoRR abs/1902.04103 (2019) - [i11]Chenguang Wang, Mu Li, Alexander J. Smola:
Language Models with Transformers. CoRR abs/1904.09408 (2019) - [i10]Haibin Lin, Hang Zhang, Yifei Ma, Tong He, Zhi Zhang, Sheng Zha, Mu Li:
Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources. CoRR abs/1904.12043 (2019) - [i9]Leyuan Wang, Zhi Chen, Yizhi Liu, Yao Wang, Lianmin Zheng, Mu Li, Yida Wang:
A Unified Optimization Approach for CNN Model Inference on Integrated GPUs. CoRR abs/1907.02154 (2019) - [i8]Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. CoRR abs/1907.04433 (2019) - 2018
- [i7]Yizhi Liu, Yao Wang, Ruofei Yu, Mu Li, Vin Sharma, Yida Wang:
Optimizing CNN Model Inference on CPUs. CoRR abs/1809.02697 (2018) - [i6]Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, Mu Li:
Bag of Tricks for Image Classification with Convolutional Neural Networks. CoRR abs/1812.01187 (2018) - 2017
- [c9]Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alexander J. Smola:
Data Driven Resource Allocation for Distributed Learning. AAAI Workshops 2017 - [c8]Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola:
Data Driven Resource Allocation for Distributed Learning. AISTATS 2017: 662-671 - 2016
- [c7]Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola:
AdaDelay: Delay Adaptive Distributed Stochastic Optimization. AISTATS 2016: 957-965 - [c6]Mu Li, Ziqi Liu, Alexander J. Smola, Yu-Xiang Wang:
DiFacto: Distributed Factorization Machines. WSDM 2016: 377-386 - [i5]Ye Yuan, Mu Li, Claire J. Tomlin:
On the Powerball Method. CoRR abs/1603.07421 (2016) - 2015
- [c5]Li Zhou, David G. Andersen, Mu Li, Alexander J. Smola:
Cuckoo Linear Algebra. KDD 2015: 1553-1562 - [c4]Mu Li, Amr Ahmed, Alexander J. Smola:
Inferring Movement Trajectories from GPS Snippets. WSDM 2015: 325-334 - [i4]Mu Li, Dave G. Andersen, Alexander J. Smola:
Graph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning. CoRR abs/1505.04636 (2015) - [i3]Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola:
AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization. CoRR abs/1508.05003 (2015) - [i2]Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, Zheng Zhang:
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. CoRR abs/1512.01274 (2015) - [i1]Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alexander J. Smola:
Data Driven Resource Allocation for Distributed Learning. CoRR abs/1512.04848 (2015) - 2014
- [c3]Mu Li, Tong Zhang, Yuqiang Chen, Alexander J. Smola:
Efficient mini-batch training for stochastic optimization. KDD 2014: 661-670 - [c2]Mu Li, David G. Andersen, Alexander J. Smola, Kai Yu:
Communication Efficient Distributed Machine Learning with the Parameter Server. NIPS 2014: 19-27 - [c1]Mu Li, David G. Andersen, Jun Woo Park, Alexander J. Smola, Amr Ahmed, Vanja Josifovski, James Long, Eugene J. Shekita, Bor-Yiing Su:
Scaling Distributed Machine Learning with the Parameter Server. OSDI 2014: 583-598
Coauthor Index
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