- Derek Driggs*, Junqi Tang*, Jingwei Liang, Mike Davies, Carola-Bibiane Schönlieb. SPRING: A Fast Stochastic Proximal Alternating Method for Non-smooth Non-convex Optimization. SIAM Journal on Imaging Sciences (to appear), 2021 [PDF][Code]
- Julian Tachella, Junqi Tang, Mike Davies. The Neural Tangent Link Between CNN Denoisers and Non-local Filters. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR, oral), 2021 [PDF][Code]
- Junqi Tang, Karen Egiazarian, Mohammad Golbabaee, Mike Davies. The Practicality of Stochastic Optimization in Imaging Inverse Problems. IEEE Transactions on Computational Imaging, 2020 [PDF]
- Junqi Tang, Karen Egiazarian, Mike Davies. The Limitation and Practical Acceleration of Stochastic Gradient Algorithms in Inverse Problems. International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 [PDF] [slides]
- Junqi Tang, Mohammad Golbabaee, Francis Bach, Mike Davies. Rest-Katyusha: Exploiting the Solution’s Structure via Scheduled Restart Schemes. Advances in Neural Information Processing Systems (NeurIPS), 2018.[PDF] [Poster]
- Junqi Tang, Mohammad Golbabaee, Mike Davies. Exploiting the Structure via Sketched Gradient Algorithms. IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2017.[PDF]
- Junqi Tang, Mohammad Golbabaee, Mike Davies. Gradient Projection Iterative Sketch for Large-Scale Constrained Least-Squares. International Conference on Machine Learning (ICML), 2017.[PDF] [slides]
In preparation / Under review:
- Junqi Tang, Mike Davies. A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problems. 2021 [Preprint]
- Bin Qian, Zhenyu Wen, Junqi Tang, Ye Yuan, Albert Zomaya, Rajiv Ranjan. OsmoticGate: Adaptive Edge-based Real-time Video Analytics for the Internet of Things. 2021 [Preprint]
