ABOUT ME

I am currently a Ph.D. student at Peking University supervised by Prof. Zhouchen Lin. Previously I obtained my B.S. degree in computer science and technology and B.S. degree in psychology from Peking University in 2020.

My research interests lie in machine learning, including deep learning, optimization, brain-inspired models, as well as their application in computer vision, image processing, and intersection with neuroscience.

In the summer 2019, I was a visiting student at CCVL (Computational Cognition, Vision, and Learning) lab in Johns Hopkins University, supervised by Prof. Alan Yuille.

From Sep. 2019 to Jun. 2020, I was a research intern at Microsoft Research Asia Machine Learning Group led by Dr. Tie-Yan Liu.

Publications

Conference Papers

1. Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks [openreview] [arxiv] [code]
   Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin
   International Conference on Learning Representations (ICLR), 2024

2. Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks [arxiv] [code]
   Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo
   International Conference on Computer Vision (ICCV), 2023

3. Online Training Through Time for Spiking Neural Networks [openreview] [arxiv] [code]
   Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin
   Advances in Neural Information Processing Systems (NeurIPS), 2022 (Spotlight)

4. Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation [link] [arXiv] [code]
   Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo
   IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022

5. Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State [openreview] [arxiv] [code]
   Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin
   Advances in Neural Information Processing Systems (NeurIPS), 2021 (Spotlight, Top 3%)

6. Invertible Image Rescaling [link] [arXiv] [code]
   Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Jiang Bian, Guolin Ke, Zhouchen Lin, Tie-Yan Liu
   European Conference on Computer Vision (ECCV), 2020 (Oral, Top 2%)

7. TDMPNet: Prototype Network with Recurrent Top-Down Modulation for Robust Object Classification under Partial Occlusion [link]
   Mingqing Xiao, Adam Kortylewski, Ruihai Wu, Siyuan Qiao, Wei Shen, Alan Yuille
   European Conference on Computer Vision Workshops (ECCVW), 2020

Journal Papers

1. Designing Universally-Approximating Deep Neural Networks: A First-Order Optimization Approach [link]
   Zhoutong Wu, Mingqing Xiao, Cong Fang, Zhouchen Lin
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024

2. Sampling Complex Topology Structures for Spiking Neural Networks [link]
   Shen Yan, Qingyan Meng, Mingqing Xiao, Yisen Wang, Zhouchen Lin
   Neural Networks, 2024

3. Efficient and Generalizable Cross-patient Epileptic Seizure Detection through A Spiking Neural Network [link]
   Zongpeng Zhang, Mingqing Xiao, Taoyun Ji, Yuwu Jiang, Tong Lin, Xiaohua Zhou, Zhouchen Lin
   Frontiers in Neuroscience (section Neuromorphic Engineering), 2024

4. Cross-patient automatic epileptic seizure detection using patient-adversarial neural networks with spatio-temporal EEG augmentation [link]
   Zongpeng Zhang, Taoyun Ji, Mingqing Xiao, Wen Wang, Guojing Yu, Tong Lin, Yuwu Jiang, Xiaohua Zhou, Zhouchen Lin
   Biomedical Signal Processing and Control, 2024

5. SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks [link] [arxiv] [code]
   Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin
   Neural Networks, 2023

6. Invertible Rescaling Network and Its Extensions [link] [arxiv] [code]
   Mingqing Xiao, Shuxin Zheng, Chang Liu, Zhouchen Lin, Tie-Yan Liu
   International Journal of Computer Vision (IJCV), 2022

7. Training Much Deeper Spiking Neural Networks with A Small Number of Time-Steps [link]
   Qingyan Meng, Shen Yan, Mingqing Xiao, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo
   Neural Networks, 2022

8. Training Neural Networks by Lifted Proximal Operator Machines [link]
   Jia Li, Mingqing Xiao, Cong Fang, Yue Dai, Chao Xu, Zhouchen Lin
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020

Academic Service

Reviewer: NeurIPS (2021-2023), ICML (2022-2024), ICLR (2023-2024), CVPR (2023-2024), ICCV (2023), ECCV (2024), IJCV, TNNLS, Neural Networks, TCSVT, Neurocomputing