Full publication lists in Google Scholar
“____” denotes the student author under my supervision.
Journal Publications
1. [25’Neurocomputing, corresponding author] J. Guo, L. Li, H. Sun, M. Qin, H. Yu, T. Zhang, “A min-max optimization framework for sparse multi-task deep neural network”, Neurocomputing, 2025. (Impact Factor: 6.5)
2. [21’TNNLS] T. Zhang, S. Ye, X. Feng, X. Ma, K. Zhang, Z. Li, et al., “StructADMM: Achieving Ultra-High Efficiency in Structured Pruning for DNNs”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. (Impact Factor: 8.9)
Conference Publications
3. [25’ACMMM, corresponding author] H. Yang, L. Li, J. Guo, B. Li, M. Qin, H. Yu, T. Zhang, “DA3D: Domain-Aware Dynamic Adaptation for All-Weather Multimodal 3D Detection”, to appear in ACM International Conference on Multimedia (ACMMM), 2025. (Acceptance Rate: 26.5%)
4. [25’AAAI, corresponding author] L. Li, H. Yang, J. Guo, H. Yu, M. Qin, T. Zhang, “An Efficient and Accurate Dynamic Sparse Training Framework Based on Parameter-freezing”, In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2025. (Acceptance Rate: 23.4%)
5. [25’ICASSP, corresponding author] J. Guo, L. Li, H. Yang, B. Geng, H. Yu, M. Qin, T. Zhang, “Robust Multi-task Adversarial Attacks Using Min-max Optimization”, In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025.
6. [24’ISCAS, corresponding author] J. Guo, H. Sun, M. Qin, H. Yu, T. Zhang, “A Min-Max Optimization Framework for Multi-task Deep Neural Network Compression”, In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), 2024.
7. [23’ICASSP, corresponding author] J Li, T Zhang, S Jin, R Zafarani, “Semi-Supervised Graph Ultra-Sparsifier Using Reweighted ℓ1 Optimization”, In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. (Acceptance Rate: 45.1%)
8. [22’ICTAI, co-first author] M. Qin, T. Zhang, F. Sun, YK. Chen, M. Fardad, Y. Wang, Y. Xie, “Compact Multi-level Sparse Neural Networks with Input Independent Dynamic Rerouting”, In Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2022. (Acceptance Rate: 15.7%)
9. [21’Neurips, co-first author] J. Wang, T. Zhang, S. Liu, PY. Chen, J. Xu, M. Fardad, B. Li, “Adversarial Attack and Robustness Exploration Empowered by Min-Max Optimization”, In Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS), 2021. (Acceptance Rate: 25.7%)
10. [21’DAC] T. Zhang, X. Ma, Z. Zhan, S. Zhou, C. Ding, M. Fardad, Y. Wang, "A Unified DNN Weight Pruning Framework Using Reweighted Optimization Methods", In Proceedings of the ACM/IEEE Design Automation Conference (DAC), 2021. (Acceptance Rate: 23.0%)
11. [20’ACC] T. Zhang, M. Fardad, “On the Optimal Interdiction of Transportation Networks”, In Proceedings of the American Control Conference (ACC), 2020.
12. [20’PAKDD] J. Li, T. Zhang, H. Tian, S. Jin, M. Fardad, R. Zafarani, “SGCN: A Graph Sparsifier Based on Graph Convolutional Networks”, In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2020. (Acceptance Rate: 21.5%)
13. [19’ICDM] T. Zhang, S. Liu, Y. Wang, M. Fardad, “Generation of Low Distortion Adversarial Attacks via Convex Programming”, In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2019. (Acceptance Rate: 18.5%)
14. [19’ASPLOS, co-first author] A. Ren, T. Zhang, S. Ye, J. Li, W. Xu, X. Qian, X. Lin, Y. Wang, “ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Methods of Multipliers”, In Proceedings of the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2019. (Acceptance Rate: 22.9%)
15. [18’ECCV] T. Zhang, S. Ye, K. Zhang, J. Tang, W. Wen, M. Fardad, Y. Wang, “A Systematic DNN Weight Pruning Framework using Alternating Method of Multipliers”, In Proceedings of the European Conference on Computer Vision (ECCV), 2018. (Acceptance Rate: 31.8%)