| Structure invariant transformation for better adversarial transferability X Wang, Z Zhang, J Zhang ICCV 2023, 2023 | 64 | 2023 |
| Video understanding with large language models: A survey Y Tang, J Bi, S Xu, L Song, S Liang, T Wang, D Zhang, J An, J Lin, R Zhu, ... arXiv preprint arXiv:2312.17432, 2023 | 63 | 2023 |
| Triangle Attack: A Query-efficient Decision-based Adversarial Attack X Wang, Z Zhang, K Tong, D Gong, K He, Z Li, W Liu ECCV 2022, 2022 | 63 | 2022 |
| Diversifying the High-level Features for better Adversarial Transferability Z Wang*, Z Zhang* (equal contribution), S Liang, X Wang BMVC 2023, 2023 | 22 | 2023 |
| Noise Optimization for Artificial Neural Networks L Xiao, Z Zhang, J Jiang, Y Peng CASE 2022, T-ASE 2024, 2021 | 22 | 2021 |
| One Forward is Enough for Neural Network Training via Likelihood Ratio Method J Jiang*, Z Zhang* (equal contribution), C Xu, Z Yu, Y Peng ICLR 2024, 2024 | 12* | 2024 |
| High-Performance Tensor Learning Primitives Using GPU Tensor Cores XY Liu*, Z Zhang* (equal contribution), Z Wang, H Lu, X Wang, A Walid IEEE Transactions on Computers, 2022 | 11 | 2022 |
| Random Smooth-based Certified Defense against Text Adversarial Attack Z Zhang*, W Yao*, S Liang, C Xu EACL 2024, 2024 | 9 | 2024 |
| Classical Simulation of Quantum Circuits: Parallel Environments and Benchmark XY Liu, Z Zhang NeurlPS 2023 Datasets and Benchmarks Track, 2023 | 7 | 2023 |
| Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts Z Zhang, X Liu, H Cheng, C Xu, J Gao arXiv preprint arXiv:2407.09590, 2024 | 6 | 2024 |
| Discover and Mitigate Multiple Biased Subgroups in Image Classifiers Z Zhang*, M Feng*, Z Li, C Xu CVPR 2024, 2024 | 6 | 2024 |
| Bag of tricks to boost adversarial transferability Z Zhang, W Yao, X Wang arXiv preprint arXiv:2401.08734, 2024 | 6 | 2024 |
| Learning to Transform Dynamically for Better Adversarial Transferability R Zhu*, Z Zhang* (Project lead), S Liang, Z Liu, C Xu CVPR, 24273-24283, 2024 | 6 | 2024 |
| Treat Visual Tokens as Text? But Your MLLM Only Needs Fewer Efforts to See Z Zhang, P Pham, W Zhao, K Wan, YJ Li, D Miranda, A Kale, C Xu arXiv preprint arXiv:2410.06169, 2024 | 5* | 2024 |
| How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies E Small, W Shao, Z Zhang, P Liu, J Chan, K Sokol, F Salim Data Mining and Knowledge Discovery, 2022 | 4 | 2022 |
| Do More Details Always Introduce More Hallucinations in LVLM-based Image Captioning? M Feng, Y Tang, Z Zhang, C Xu arXiv preprint arXiv:2406.12663, 2024 | 3 | 2024 |
| Understanding Model Ensemble in Transferable Adversarial Attack W Yao, Z Zhang (equal contribution), H Tang, Y Liu arXiv preprint arXiv:2410.06851, 2024 | 2 | 2024 |
| High-Performance Tensor-Train Primitives Using GPU Tensor Cores XY Liu, H Hong, Z Zhang, W Tong, J Kossaifi, X Wang, A Walid IEEE Transactions on Computers, 2024 | 2 | 2024 |
| VidComposition: Can MLLMs Analyze Compositions in Compiled Videos? Y Tang, J Guo, H Hua, S Liang, M Feng, X Li, R Mao, C Huang, J Bi, ... CVPR 2025; arXiv preprint arXiv:2411.10979, 2024 | 1 | 2024 |
| Will the Inclusion of Generated Data Amplify Bias Across Generations in Future Image Classification Models? Z Zhang, X Liang, M Feng, S Liang, C Xu arXiv preprint arXiv:2410.10160, 2024 | 1 | 2024 |
Chenliang XuAssociate Professor of Computer Science, University of RochesterPreverjeni e-poštni naslov na rochester.edu
