Mu Li

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 76

Top Topics

Semantic Segmentation

Top Venues

Publications

Peng Zhou, Shuai Zhang, Mu Li, Yuanting Yan
Online learning from capricious data streams via shared and new feature spaces. Appl. Intell. 54 (19) (2024)
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)
Yi Zhu, Zhongyue Zhang, Chongruo Wu, Zhi Zhang, Tong He, Hang Zhang, R. Manmatha, Mu Li, Alexander Smola
Improving Semantic Segmentation via Efficient Self-Training. IEEE Trans. Pattern Anal. Mach. Intell. 46 (3) (2024)
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)
Jiaao Chen, Aston Zhang, Mu Li, Alex Smola, Diyi Yang
A Cheaper and Better Diffusion Language Model with Soft-Masked Noise. CoRR (2023)
Jiaxin Cheng, Xiao Liang, Xingjian Shi, Tong He, Tianjun Xiao, Mu Li
LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation. CoRR (2023)
Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, Mahsa Shoaran
XTab: Cross-table Pretraining for Tabular Transformers. ICML (2023)
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 (2023)
Matías Mendieta, Boran Han, Xingjian Shi, Yi Zhu, Chen Chen, Mu Li
GFM: Building Geospatial Foundation Models via Continual Pretraining. CoRR (2023)
Shengju Qian, Yi Zhu, Wenbo Li, Mu Li, Jiaya Jia
What Makes for Good Tokenizers in Vision Transformer? IEEE Trans. Pattern Anal. Mach. Intell. 45 (11) (2023)
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)
Jiaao Chen, Aston Zhang, Mu Li, Alex Smola, Diyi Yang
A Cheaper and Better Diffusion Language Model with Soft-Masked Noise. EMNLP (2023)
Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, Alex Smola
Multimodal Chain-of-Thought Reasoning in Language Models. CoRR (2023)
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 (2023)
Taojiannan Yang, Yi Zhu, Yusheng Xie, Aston Zhang, Chen Chen, Mu Li
AIM: Adapting Image Models for Efficient Video Action Recognition. CoRR (2023)
Yuxin Ren, Zihan Zhong, Xingjian Shi, Yi Zhu, Chun Yuan, Mu Li
Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation. CoRR (2023)
Taojiannan Yang, Yi Zhu, Yusheng Xie, Aston Zhang, Chen Chen, Mu Li
AIM: Adapting Image Models for Efficient Video Action Recognition. ICLR (2023)
Zhuosheng Zhang, Aston Zhang, Mu Li, Alex Smola
Automatic Chain of Thought Prompting in Large Language Models. ICLR (2023)
Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, Mahsa Shoaran
XTab: Cross-table Pretraining for Tabular Transformers. CoRR (2023)
Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson
Learning Multimodal Data Augmentation in Feature Space. ICLR (2023)
Jiaao Chen, Aston Zhang, Xingjian Shi, Mu Li, Alex Smola, Diyi Yang
Parameter-Efficient Fine-Tuning Design Spaces. ICLR (2023)
Xiaoshuai Hao, Yi Zhu, Srikar Appalaraju, Aston Zhang, Wanqian Zhang, Bo Li, Mu Li
MixGen: A New Multi-Modal Data Augmentation. WACV (Workshops) (2023)
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)
Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi, Mu Li, Zhangyang Wang
Removing Batch Normalization Boosts Adversarial Training. ICML (2022)
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 (2022)
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) (2022)
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 (2022)
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)
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 (2022)
Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi, Mu Li, Zhangyang Wang
Removing Batch Normalization Boosts Adversarial Training. CoRR (2022)
Xiaoshuai Hao, Yi Zhu, Srikar Appalaraju, Aston Zhang, Wanqian Zhang, Bo Li, Mu Li
MixGen: A New Multi-Modal Data Augmentation. CoRR (2022)
Likun Cai, Zhi Zhang, Yi Zhu, Li Zhang, Mu Li, Xiangyang Xue
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training. CoRR (2022)
Fangrui Zhu, Yi Zhu, Li Zhang, Chongruo Wu, Yanwei Fu, Mu Li
A Unified Efficient Pyramid Transformer for Semantic Segmentation. CoRR (2021)
Li Wang, Li Zhang, Yi Zhu, Zhi Zhang, Tong He, Mu Li, Xiangyang Xue
Progressive Coordinate Transforms for Monocular 3D Object Detection. CoRR (2021)
Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander J. Smola
Benchmarking Multimodal AutoML for Tabular Data with Text Fields. CoRR (2021)
Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris N. Metaxas
SelfNorm and CrossNorm for Out-of-Distribution Robustness. CoRR (2021)
Li Wang, Li Zhang, Yi Zhu, Zhi Zhang, Tong He, Mu Li, Xiangyang Xue
Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS (2021)
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 (2021)
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)
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)
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)
Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris N. Metaxas
CrossNorm and SelfNorm for Generalization under Distribution Shifts. ICCV (2021)
Fangrui Zhu, Yi Zhu, Li Zhang, Chongruo Wu, Yanwei Fu, Mu Li
A Unified Efficient Pyramid Transformer for Semantic Segmentation. ICCVW (2021)
Haofei Kuang, Yi Zhu, Zhi Zhang, Xinyu Li, Joseph Tighe, Sören Schwertfeger, Cyrill Stachniss, Mu Li
Video Contrastive Learning with Global Context. ICCVW (2021)
Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola
Dive into Deep Learning. CoRR (2021)
Haofei Kuang, Yi Zhu, Zhi Zhang, Xinyu Li, Joseph Tighe, Sören Schwertfeger, Cyrill Stachniss, Mu Li
Video Contrastive Learning with Global Context. CoRR (2021)
Cody Hao Yu, Xingjian Shi, Haichen Shen, Zhi Chen, Mu Li, Yida Wang
Lorien: Efficient Deep Learning Workloads Delivery. SoCC (2021)
Shengju Qian, Hao Shao, Yi Zhu, Mu Li, Jiaya Jia
Blending Anti-Aliasing into Vision Transformer. CoRR (2021)
Shengju Qian, Hao Shao, Yi Zhu, Mu Li, Jiaya Jia
Blending Anti-Aliasing into Vision Transformer. NeurIPS (2021)
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 (2020)
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 (2020)
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 (2020)
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 (2020)
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 (2020)
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 (2020)
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. SC (2020)
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 (2020)
Yizhi Liu, Yao Wang, Ruofei Yu, Mu Li, Vin Sharma, Yida Wang
Optimizing CNN Model Inference on CPUs. USENIX Annual Technical Conference (2019)
Chenguang Wang, Mu Li, Alexander J. Smola
Language Models with Transformers. CoRR (2019)
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)
Zhi Zhang, Tong He, Hang Zhang, Zhongyue Zhang, Junyuan Xie, Mu Li
Bag of Freebies for Training Object Detection Neural Networks. CoRR (2019)
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 (2019)
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 (2019)
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 (2019)
Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, Mu Li
Bag of Tricks for Image Classification with Convolutional Neural Networks. CVPR (2019)
Yizhi Liu, Yao Wang, Ruofei Yu, Mu Li, Vin Sharma, Yida Wang
Optimizing CNN Model Inference on CPUs. CoRR (2018)
Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, Mu Li
Bag of Tricks for Image Classification with Convolutional Neural Networks. CoRR (2018)
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)
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola
Data Driven Resource Allocation for Distributed Learning. AISTATS (2017)
Mu Li, Ziqi Liu, Alexander J. Smola, Yu-Xiang Wang
DiFacto: Distributed Factorization Machines. WSDM (2016)
Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola
AdaDelay: Delay Adaptive Distributed Stochastic Optimization. AISTATS (2016)
Li Zhou, David G. Andersen, Mu Li, Alexander J. Smola
Cuckoo Linear Algebra. KDD (2015)
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alexander J. Smola
Data Driven Resource Allocation for Distributed Learning. CoRR (2015)
Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola
AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization. CoRR (2015)
Mu Li, Amr Ahmed, Alexander J. Smola
Inferring Movement Trajectories from GPS Snippets. WSDM (2015)
Mu Li, Dave G. Andersen, Alexander J. Smola
Graph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning. CoRR (2015)
Mu Li, David G. Andersen, Alexander J. Smola, Kai Yu
Communication Efficient Distributed Machine Learning with the Parameter Server. NIPS (2014)
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)
Mu Li, Tong Zhang, Yuqiang Chen, Alexander J. Smola
Efficient mini-batch training for stochastic optimization. KDD (2014)