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Journal Articles
- 2024
[j4]Li Shen
, Congliang Chen, Fangyu Zou, Zequn Jie, Ju Sun, Wei Liu:
A Unified Analysis of AdaGrad With Weighted Aggregation and Momentum Acceleration. IEEE Trans. Neural Networks Learn. Syst. 35(10): 14482-14490 (2024)- 2023
- [j3]Congliang Chen, Li Shen, Wei Liu, Zhi-Quan Luo:
Efficient-Adam: Communication-Efficient Distributed Adam. IEEE Trans. Signal Process. 71: 3257-3266 (2023) - 2022
- [j2]Congliang Chen, Li Shen, Fangyu Zou, Wei Liu:
Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration. J. Mach. Learn. Res. 23: 229:1-229:47 (2022) - 2021
- [j1]Congliang Chen, Li Shen, Hao-Zhi Huang, Wei Liu:
Quantized Adam with Error Feedback. ACM Trans. Intell. Syst. Technol. 12(5): 56:1-56:26 (2021)
Conference and Workshop Papers
- 2022
- [c4]Yushun Zhang, Congliang Chen, Naichen Shi, Ruoyu Sun, Zhi-Quan Luo:
Adam Can Converge Without Any Modification On Update Rules. NeurIPS 2022 - 2021
- [c3]Congliang Chen, Jiawei Zhang, Li Shen, Peilin Zhao, Zhi-Quan Luo:
Communication Efficient Primal-Dual Algorithm for Nonconvex Nonsmooth Distributed Optimization. AISTATS 2021: 1594-1602 - 2018
- [c2]Shuyang Gu, Congliang Chen, Jing Liao, Lu Yuan:
Arbitrary Style Transfer With Deep Feature Reshuffle. CVPR 2018: 8222-8231 - 2017
- [c1]Zhiwei Fan, Congliang Chen, Tingting Jiang:
A semi-automatic editing method for surgery videos. ICME Workshops 2017: 115-120
Informal and Other Publications
- 2024
- [i9]Yushun Zhang, Congliang Chen, Tian Ding, Ziniu Li, Ruoyu Sun, Zhi-Quan Luo:
Why Transformers Need Adam: A Hessian Perspective. CoRR abs/2402.16788 (2024) - [i8]Yushun Zhang, Congliang Chen, Ziniu Li, Tian Ding, Chenwei Wu, Yinyu Ye, Zhi-Quan Luo, Ruoyu Sun:
Adam-mini: Use Fewer Learning Rates To Gain More. CoRR abs/2406.16793 (2024) - [i7]Ziniu Li, Congliang Chen, Tian Xu, Zeyu Qin, Jiancong Xiao, Ruoyu Sun, Zhi-Quan Luo:
Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity. CoRR abs/2408.16673 (2024) - 2023
- [i6]Tao Sun, Congliang Chen, Peng Qiao, Li Shen, Xinwang Liu, Dongsheng Li:
Rethinking SIGN Training: Provable Nonconvex Acceleration without First- and Second-Order Gradient Lipschitz. CoRR abs/2310.14616 (2023) - 2022
- [i5]Congliang Chen, Li Shen, Wei Liu, Zhi-Quan Luo:
Efficient-Adam: Communication-Efficient Distributed Adam with Complexity Analysis. CoRR abs/2205.14473 (2022) - [i4]Yushun Zhang, Congliang Chen, Naichen Shi, Ruoyu Sun, Zhi-Quan Luo:
Adam Can Converge Without Any Modification on Update Rules. CoRR abs/2208.09632 (2022) - 2021
- [i3]Congliang Chen, Li Shen, Fangyu Zou, Wei Liu:
Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration. CoRR abs/2101.05471 (2021) - 2020
- [i2]Congliang Chen, Li Shen, Haozhi Huang, Wei Liu:
Quantized Adam with Error Feedback. CoRR abs/2004.14180 (2020) - 2018
- [i1]Shuyang Gu, Congliang Chen, Jing Liao, Lu Yuan:
Arbitrary Style Transfer with Deep Feature Reshuffle. CoRR abs/1805.04103 (2018)
Coauthor Index
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