- Hong Ye Tan, Subhadip Mukherjee, Junqi Tang, Carola-Bibiane Schönlieb. Data-Driven Mirror Descent with Input-Convex Neural Networks. SIAM Journal on Mathematics of Data Science, 2023 [PDF]
- Hong Ye Tan, Subhadip Mukherjee, Junqi Tang, Andreas Hauptmann, Carola-Bibiane Schönlieb. Robust Data-Driven Accelerated Mirror Descent. International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 [PDF]
- 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. IEEE Transactions on Computers, 2022 [PDF]
- Derek Driggs*, Junqi Tang*, Jingwei Liang, Mike Davies, Carola-Bibiane Schönlieb. Stochastic Proximal Alternating Minimization for Non-smooth and Non-convex Optimization. SIAM Journal on Imaging Sciences, 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]
Preprints/Technical reports:
- Hong Ye Tan, Subhadip Mukherjee, Junqi Tang, Carola-Bibiane Schönlieb. Provably Convergent Plug-and-Play Quasi-Newton Methods. Technical Report, 2023 [Preprint]
- Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb. Accelerating Deep Unrolling Networks via Dimensionality Reduction. Technical report, 2022 [Preprint][Presentation@IMA Big-Data Conf]
- Junqi Tang. Data-Consistent Local Superresolution for Medical Imaging. 2022 [Preprint]
JT 