陈军，博士，浙江师范大学“双龙学者”特聘教授，计算机科学与技术学院硕士生导师。主要研究方向为深度学习压缩与加速、深度学习理论和分布式优化，相关成果于2021年获得浙江省科技进步奖一等奖。近五年，在JMLR、TPAMI、TNNLS、TSP、TOG等计算机领域权威期刊，以及ICCV、ECCV、AAAI、ICLR、NeurIPS等计算机领域顶级会议发表论文40余篇，其中第一作者/通讯作者20篇。更多内容请关注Google Scholar主页：https://scholar.google.com/citations?user=YKc2O78AAAAJ&hl=en

1.1期刊论文

1. Jun Chen*, Tianyi Zhu, Haishan Ye, Lina Liu, Guang Dai, Yong Liu, Yunliang Jiang, and Ivor W Tsang. “Riemannian Momentum Tracking: Distributed Optimization with Momentum on Compact Submanifolds.” IEEE Transactions on Control of Network Systems, 2026.

2. Siqi Li, Jingyang Xiang, Jiateng Wei, Chengrui Zhu, Jiandang Yang, Jun Chen*, Jian Yang, Xiaobin Wei, Yunliang Jiang, and Yong Liu. “WLR: Well-conditioned Linear Reconstruction for Retraining-free Pruning of LLMs.” Neural Networks, 2026, 200: 108840.

3. Jiateng Wei, Siqi Li, Jingyang Xiang, Jiandang Yang, Jun Chen*, Xiaobin Wei, Yunliang Jiang, and Yong Liu. “OOPS: Outlier-Aware and Quadratic Programming Based Structured Pruning for Large Language Models.” Neural Networks, 2026, 196: 108332.

4. Jun Chen*, Lina Liu, Tianyi Zhu, Yong Liu, Guang Dai, Yunliang Jiang, and Ivor W Tsang “Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking." IEEE Transactions on Signal Processing, 2025, 73: 1851-1861.

5. Jun Chen*, Jingyang Xiang, Tianxin Huang, Xiangrui Zhao, and Yong Liu. “Hyperbolic Binary Neural Network.” IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(6): 10325-10333.

6. Muxuan Gao, Juntao Jiang, Shuangming Lei, Huifeng Wu, Jun Chen*, and Yong Liu. “OnSort: An O (n) Comparison-Free Sorter for Large-Scale Dataset With Parallel Prefetching and Sparse-Aware Mechanism.” IEEE Transactions on Circuits and Systems II: Express Briefs, 2025, 72(7): 933-937.

7. Siqi Li, Jun Chen, Shanqi Liu, Chengrui Zhu, Guanzhong Tian, and Yong Liu. “MCMC: Multi-Constrained Model Compression via One-Stage Envelope Reinforcement Learning.” IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(2): 3410-3422.

8. Guanzhong Tian, Yiran Sun, Yuang Liu, Xianfang Zeng, Mengmeng Wang, Yong Liu, Jiangning Zhang, and Jun Chen. "Adding before pruning: Sparse filter fusion for deep convolutional neural networks via auxiliary attention." IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(3): 3930-3942.

9. Jun Chen, Hanwen Chen, Mengmeng Wang, Guang Dai, Ivor W. Tsang, and Yong Liu. “Learning Discretized Neural Networks under Ricci Flow.” Journal of Machine Learning Research, 2024, 25(386): 1-44.

10. Tianyi Zhu, Lina Liu, Yibo Sun, Zhi Lu, Yuanlong Zhang, Chao Xu, and Jun Chen*. "Semi-supervised noise-resilient anomaly detection with feature autoencoder." Knowledge-Based Systems, 2024, 304: 112445.

11. Shanqi Liu, Weiwei Liu, Wenzhou Chen, Guanzhong Tian, Jun Chen, Yao Tong, Junjie Cao, and Yong Liu. "Learning multi-agent cooperation via considering actions of teammates." IEEE Transactions on Neural Networks and Learning Systems, 2023, 35(8): 11553-11564.

12. Yuang Liu¹, Jun Chen¹, and Yong Liu. "DCCD: Reducing Neural Network Redundancy via Distillation." IEEE Transactions on Neural Networks and Learning Systems, 2023, 35(7): 10006-10017.

13. Tianxin Huang, Jiangning Zhang, Jun Chen, Zhonggan Ding, Ying Tai, Zhenyu Zhang, Chengjie Wang, and Yong Liu. "3qnet: 3d point cloud geometry quantization compression network." ACM Transactions on Graphics (TOG), 2022, 41(6): 1-13.

14. Mengmeng Wang, Jiazheng Xing, Jing Su, Jun Chen, and Yong Liu. "Learning spatiotemporal and motion features in a unified 2d network for action recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(3): 3347-3362.

15. Jun Chen, Liang Liu, Yong Liu, and Xianfang Zeng. "A learning framework for n-bit quantized neural networks toward FPGAs." IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(3): 1067-1081.

16. Guanzhong Tian, Yiran Sun, Yuang Liu, Xianfang Zeng, Mengmeng Wang, Yong Liu, Jiangning Zhang, and Jun Chen. "Adding before pruning: Sparse filter fusion for deep convolutional neural networks via auxiliary attention." IEEE Transactions on Neural Networks and Learning Systems, 2021.

17. Jun Chen, Yong Liu, Hao Zhang, Shengnan Hou, and Jian Yang. "Propagating asymptotic-estimated gradients for low bitwidth quantized neural networks." IEEE Journal of Selected Topics in Signal Processing, 2020, 14(4): 848-859.

1.2 会议论文

1. Qi Zhao, Jun Chen*, Ivor W Tsang, and Guang Dai. “RealDiffusion: Physics-informed Attention for Multi-character Storybook Generation.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Findings, pp. 4698-4707, 2026.

2. Yuzhe Yao, Jun Chen, Zeyi Huang, Haonan Lin, Mengmeng Wang, Guang Dai, and Jingdong Wang. " Manifold Constraint Reduces Exposure Bias in Accelerated Diffusion Sampling. " International Conference on Learning Representations (ICLR), 2025.

3. Jun Chen, Haishan Ye, Mengmeng Wang, Tianxin Huang, Guang Dai, Ivor W Tsang, and Yong Liu. "Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold. " International Conference on Learning Representations (ICLR), 2024.

4. Yuzhe Yao, Feng Tian, Jun Chen*, Haonan Lin, Guang Dai, Yong Liu, and Jingdong Wang. " Timestep-Aware Correction for Quantized Diffusion Models." In European Conference on Computer Vision (ECCV), pp. 215-232. Cham: Springer Nature Switzerland, 2024.

5. Jiateng Wei, Quan Lu, Ning Jiang, Siqi Li, Jingyang Xiang, Jun Chen*, and Yong Liu. "Structured Optimal Brain Pruning for Large Language Models." Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 13991-14007. 2024.

6. Jingyang Xiang, Siqi Li, Jun Chen, Guang Dai, Shipeng Bai, Yukai Ma, and Yong Liu. "SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration." In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 36, 2024.

7. Mengmeng Wang, Jiazheng Xing, Boyuan Jiang, Jun Chen, Jianbiao Mei, Xingxing Zuo, Guang Dai, Jingdong Wang, and Yong Liu. "A Multimodal, Multi-Task Adapting Framework for Video Action Recognition." In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 38, no. 6, pp. 5517-5525. 2024.

8. Xintian Shen, Jiangning Zhang, Jun Chen, Shipeng Bai, Yue Han, Yabiao Wang, Chengjie Wang, and Yong Liu. "Learning Global-aware Kernel for Image Harmonization." In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 7535-7544. 2023.

9. Tianxin Huang, Xuemeng Yang, Jiangning Zhang, Jinhao Cui, Hao Zou, Jun Chen, Xiangrui Zhao, and Yong Liu. "Learning to train a point cloud reconstruction network without matching." In European Conference on Computer Vision (ECCV), pp. 179-194. Cham: Springer Nature Switzerland, 2022.

10. Tianxin Huang, Jiangning Zhang, Jun Chen, Yuang Liu, and Yong Liu. "Resolution-free point cloud sampling network with data distillation." In European Conference on Computer Vision (ECCV), pp. 54-70. Cham: Springer Nature Switzerland, 2022.

11. Xiangrui Zhao, Sheng Yang, Tianxin Huang, Jun Chen, Teng Ma, Mingyang Li, and Yong Liu. "Superline3d: Self-supervised line segmentation and description for lidar point cloud." In European Conference on Computer Vision (ECCV), pp. 263-279. Cham: Springer Nature Switzerland, 2022.

2020.9 -- 2024.3
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