Boosting adversarial attacks with momentum Y Dong, F Liao, T Pang, H Su, J Zhu, X Hu, J Li CVPR (arXiv preprint arXiv:1710.06081, 2017), 2018 | 2961 | 2018 |
Spatio-temporal backpropagation for training high-performance spiking neural networks Y Wu, L Deng, G Li, J Zhu, L Shi Frontiers in neuroscience 12, 323875, 2018 | 959 | 2018 |
Defense against adversarial attacks using high-level representation guided denoiser F Liao, M Liang, Y Dong, T Pang, J Zhu, X Hu CVPR (arXiv preprint arXiv:1712.02976), 2018 | 958 | 2018 |
Dino: Detr with improved denoising anchor boxes for end-to-end object detection H Zhang, F Li, S Liu, L Zhang, H Su, J Zhu, LM Ni, HY Shum arXiv preprint arXiv:2203.03605, 2022 | 868 | 2022 |
Evading defenses to transferable adversarial examples by translation-invariant attacks Y Dong, T Pang, H Su, J Zhu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 843 | 2019 |
Adversarial Attack on Graph Structured Data H Dai, H Li, T Tian, X Huang, L Wang, J Zhu, L Song ICML (arXiv preprint arXiv:1806.02371), 2018 | 817 | 2018 |
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps C Lu, Y Zhou, F Bao, J Chen, C Li, J Zhu NeurIPS (arXiv preprint arXiv:2206.00927), 2022 | 735 | 2022 |
Grounding dino: Marrying dino with grounded pre-training for open-set object detection S Liu, Z Zeng, T Ren, F Li, H Zhang, J Yang, C Li, J Yang, H Su, J Zhu, ... arXiv preprint arXiv:2303.05499, 2023 | 706 | 2023 |
Pre-trained models: Past, present and future X Han, Z Zhang, N Ding, Y Gu, X Liu, Y Huo, J Qiu, Y Yao, A Zhang, ... AI Open 2, 225-250, 2021 | 637 | 2021 |
Explainable AI: A brief survey on history, research areas, approaches and challenges F Xu, H Uszkoreit, Y Du, W Fan, D Zhao, J Zhu Natural Language Processing and Chinese Computing: 8th CCF International …, 2019 | 613 | 2019 |
Direct training for spiking neural networks: Faster, larger, better Y Wu, L Deng, G Li, J Zhu, Y Xie, L Shi Proceedings of the AAAI conference on artificial intelligence 33 (01), 1311-1318, 2019 | 608 | 2019 |
Stochastic Training of Graph Convolutional Networks J Chen, J Zhu ICML (arXiv preprint arXiv:1710.10568), 2018 | 595* | 2018 |
Towards Better Analysis of Deep Convolutional Neural Networks M Liu, J Shi, Z Li, C Li, J Zhu, S Liu IEEE Transactions on Visualization & Computer Graphics, 2016 | 578 | 2016 |
MedLDA: maximum margin supervised topic models for regression and classification J Zhu, A Ahmed, EP Xing Proceedings of the 26th annual international conference on machine learning …, 2009 | 557 | 2009 |
Triple Generative Adversarial Nets C Li, K Xu, J Zhu, B Zhang NIPS (arXiv preprint arXiv:1703.02291), 2017 | 545 | 2017 |
Dab-detr: Dynamic anchor boxes are better queries for detr S Liu, F Li, H Zhang, X Yang, X Qi, H Su, J Zhu, L Zhang arXiv preprint arXiv:2201.12329, 2022 | 541 | 2022 |
Improving adversarial robustness via promoting ensemble diversity T Pang, K Xu, C Du, N Chen, J Zhu ICML (arXiv preprint arXiv:1901.08846), 2019 | 452 | 2019 |
Towards better analysis of machine learning models: A visual analytics perspective S Liu, X Wang, M Liu, J Zhu Visual Informatics 1 (1), 48-56, 2017 | 434 | 2017 |
Efficient decision-based black-box adversarial attacks on face recognition Y Dong, H Su, B Wu, Z Li, W Liu, T Zhang, J Zhu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 432 | 2019 |
Statsnowball: a statistical approach to extracting entity relationships J Zhu, Z Nie, X Liu, B Zhang, JR Wen Proceedings of the 18th international conference on World wide web, 101-110, 2009 | 401 | 2009 |