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ORCIDMinjia Zhang
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Publications
- 2025
[j6]Fengxiang Bie
, Yibo Yang, Zhongzhu Zhou, Adam Ghanem, Minjia Zhang, Zhewei Yao, Xiaoxia Wu, Connor Holmes, Pareesa Ameneh Golnari, David A. Clifton, Yuxiong He, Dacheng Tao, Shuaiwen Leon Song:
RenAIssance: A Survey Into AI Text-to-Image Generation in the Era of Large Model. IEEE Trans. Pattern Anal. Mach. Intell. 47(3): 2212-2231 (2025)- [j5]Syed Zawad, Xiaolong Ma, Jun Yi, Cheng Li, Minjia Zhang, Lei Yang, Feng Yan, Yuxiong He:
FedCust: Offloading hyperparameter customization for federated learning. Perform. Evaluation 167: 102450 (2025) - 2024
- [c51]Conglong Li, Zhewei Yao, Xiaoxia Wu, Minjia Zhang, Connor Holmes, Cheng Li, Yuxiong He:
DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing. AAAI 2024: 18490-18498 - [c49]Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, Minjia Zhang, Reza Yazdani Aminabadi, Shuaiwen Leon Song, Samyam Rajbhandari, Yuxiong He:
System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models. IPDPS (Workshops) 2024: 1206-1208 - [c46]Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, Minjia Zhang, Reza Yazdani Aminadabi, Shuaiwen Leon Song, Samyam Rajbhandari, Yuxiong He:
System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models. PODC 2024: 121-130 - 2023
- [j2]Reza Yazdani Aminabadi, Olatunji Ruwase, Minjia Zhang, Yuxiong He, José-María Arnau, Antonio González:
SHARP: An Adaptable, Energy-Efficient Accelerator for Recurrent Neural Networks. ACM Trans. Embed. Comput. Syst. 22(2): 30:1-30:23 (2023) - [c44]Minjia Zhang, Uma-Naresh Niranjan, Yuxiong He:
Revisiting the Efficiency-Accuracy Tradeoff in Adapting Transformer Models via Adversarial Fine-Tuning. ECAI 2023: 3026-3033 - [c43]Yucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He:
Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam. ICLR 2023 - [i30]Zhewei Yao, Reza Yazdani Aminabadi, Olatunji Ruwase, Samyam Rajbhandari, Xiaoxia Wu, Ammar Ahmad Awan, Jeff Rasley, Minjia Zhang, Conglong Li, Connor Holmes, Zhongzhu Zhou, Michael Wyatt, Molly Smith, Lev Kurilenko, Heyang Qin, Masahiro Tanaka, Shuai Che, Shuaiwen Leon Song, Yuxiong He:
DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales. CoRR abs/2308.01320 (2023) - [i28]Fengxiang Bie, Yibo Yang, Zhongzhu Zhou, Adam Ghanem, Minjia Zhang, Zhewei Yao, Xiaoxia Wu, Connor Holmes, Pareesa Ameneh Golnari, David A. Clifton, Yuxiong He, Dacheng Tao, Shuaiwen Leon Song:
RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model. CoRR abs/2309.00810 (2023) - [i27]Zhewei Yao, Xiaoxia Wu, Conglong Li, Minjia Zhang, Heyang Qin, Olatunji Ruwase, Ammar Ahmad Awan, Samyam Rajbhandari, Yuxiong He:
DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention. CoRR abs/2309.14327 (2023) - [i26]Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, Minjia Zhang, Shuaiwen Leon Song, Samyam Rajbhandari, Yuxiong He:
DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models. CoRR abs/2309.14509 (2023) - [i24]Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens:
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. CoRR abs/2310.04610 (2023) - 2022
- [c39]Minjia Zhang, Uma-Naresh Niranjan, Yuxiong He:
Adversarial Data Augmentation for Task-Specific Knowledge Distillation of Pre-trained Transformers. AAAI 2022: 11685-11693 - [c37]Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He:
DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale. ICML 2022: 18332-18346 - [c36]Conglong Li, Minjia Zhang, Yuxiong He:
The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models. NeurIPS 2022 - [c35]Xiaoxia Wu, Zhewei Yao, Minjia Zhang, Conglong Li, Yuxiong He:
XTC: Extreme Compression for Pre-trained Transformers Made Simple and Efficient. NeurIPS 2022 - [c34]Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, Yuxiong He:
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. NeurIPS 2022 - [c33]Reza Yazdani Aminabadi, Samyam Rajbhandari, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Olatunji Ruwase, Shaden Smith, Minjia Zhang, Jeff Rasley, Yuxiong He:
DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale. SC 2022: 46:1-46:15 - [c32]Minjia Zhang, Wenhan Wang, Yuxiong He:
GraSP: Optimizing Graph-based Nearest Neighbor Search with Subgraph Sampling and Pruning. WSDM 2022: 1395-1405 - [i23]Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He:
DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale. CoRR abs/2201.05596 (2022) - [i22]Minjia Zhang, Uma-Naresh Niranjan, Yuxiong He:
ScaLA: Accelerating Adaptation of Pre-Trained Transformer-Based Language Models via Efficient Large-Batch Adversarial Noise. CoRR abs/2201.12469 (2022) - [i20]Yucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He:
Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam. CoRR abs/2202.06009 (2022) - [i17]Xiaoxia Wu, Zhewei Yao, Minjia Zhang, Conglong Li, Yuxiong He:
Extreme Compression for Pre-trained Transformers Made Simple and Efficient. CoRR abs/2206.01859 (2022) - [i16]Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, Yuxiong He:
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. CoRR abs/2206.01861 (2022) - [i15]Connor Holmes, Minjia Zhang, Yuxiong He, Bo Wu:
Compressing Pre-trained Transformers via Low-Bit NxM Sparsity for Natural Language Understanding. CoRR abs/2206.15014 (2022) - [i14]Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, Yuxiong He:
DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale. CoRR abs/2207.00032 (2022) - [i13]Zhewei Yao, Xiaoxia Wu, Conglong Li, Connor Holmes, Minjia Zhang, Cheng Li, Yuxiong He:
Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers. CoRR abs/2211.11586 (2022) - [i12]Conglong Li, Zhewei Yao, Xiaoxia Wu, Minjia Zhang, Yuxiong He:
DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing. CoRR abs/2212.03597 (2022) - 2021
- [c26]Connor Holmes, Minjia Zhang, Yuxiong He, Bo Wu:
NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM. NeurIPS 2021: 1818-1830 - [c25]Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, Yuxiong He:
ZeRO-Offload: Democratizing Billion-Scale Model Training. USENIX ATC 2021: 551-564 - [i11]Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, Yuxiong He:
ZeRO-Offload: Democratizing Billion-Scale Model Training. CoRR abs/2101.06840 (2021) - [i9]Conglong Li, Minjia Zhang, Yuxiong He:
Curriculum Learning: A Regularization Method for Efficient and Stable Billion-Scale GPT Model Pre-Training. CoRR abs/2108.06084 (2021) - [i7]Connor Holmes, Minjia Zhang, Yuxiong He, Bo Wu:
NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM. CoRR abs/2110.15766 (2021) - 2020
- [c21]Minjia Zhang, Yuxiong He:
Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping. NeurIPS 2020 - [c20]Conglong Li, Minjia Zhang, David G. Andersen, Yuxiong He:
Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination. SIGMOD Conference 2020: 2539-2554 - [i5]Minjia Zhang, Yuxiong He:
Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping. CoRR abs/2010.13369 (2020) - 2019
- [c19]Minjia Zhang, Yuxiong He:
GRIP: Multi-Store Capacity-Optimized High-Performance Nearest Neighbor Search for Vector Search Engine. CIKM 2019: 1673-1682 - [c18]Minjia Zhang, Samyam Rajbhandari, Wenhan Wang, Elton Zheng, Olatunji Ruwase, Jeff Rasley, Jason Li, Junhua Wang, Yuxiong He:
Accelerating Large Scale Deep Learning Inference through DeepCPU at Microsoft. OpML 2019: 5-7 - [i3]Reza Yazdani, Olatunji Ruwase, Minjia Zhang, Yuxiong He, José-María Arnau, Antonio González:
LSTM-Sharp: An Adaptable, Energy-Efficient Hardware Accelerator for Long Short-Term Memory. CoRR abs/1911.01258 (2019) - 2018
- [c16]Wei Wen, Yuxiong He, Samyam Rajbhandari, Minjia Zhang, Wenhan Wang, Fang Liu, Bin Hu, Yiran Chen, Hai Li:
Learning Intrinsic Sparse Structures within Long Short-Term Memory. ICLR (Poster) 2018 - [c14]Minjia Zhang, Wenhan Wang, Xiaodong Liu, Jianfeng Gao, Yuxiong He:
Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models. NeurIPS 2018: 6311-6322 - [c13]Minjia Zhang, Samyam Rajbhandari, Wenhan Wang, Yuxiong He:
DeepCPU: Serving RNN-based Deep Learning Models 10x Faster. USENIX ATC 2018: 951-965 - [i2]Minjia Zhang, Xiaodong Liu, Wenhan Wang, Jianfeng Gao, Yuxiong He:
Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models. CoRR abs/1806.04189 (2018) - [i1]Minjia Zhang, Yuxiong He:
Zoom: SSD-based Vector Search for Optimizing Accuracy, Latency and Memory. CoRR abs/1809.04067 (2018)
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