| Cluster Alignment with a Teacher for Unsupervised Domain Adaptation Z Deng, Y Luo, J Zhu IEEE International Conference on Computer Vision 2019, 2019 | 218 | 2019 |
| Adversarial distributional training for robust deep learning Y Dong, Z Deng, T Pang, H Su, J Zhu Advances in Neural Information Processing Systems 33, 2020 | 91 | 2020 |
| Batch Virtual Adversarial Training for Graph Convolutional Networks Z Deng, Y Dong, J Zhu AI Open, 2023 | 79 | 2023 |
| Black-box Detection of Backdoor Attacks with Limited Information and Data Y Dong, X Yang, Z Deng, T Pang, Z Xiao, H Su, J Zhu ICCV 2021, 2021 | 61 | 2021 |
| Structured generative adversarial networks Z Deng, H Zhang, X Liang, L Yang, S Xu, J Zhu, EP Xing Advances in Neural Information Processing Systems, 2017 | 61 | 2017 |
| Exploring memorization in adversarial training Y Dong, K Xu, X Yang, T Pang, Z Deng, H Su, J Zhu ICLR 2022, 2022 | 42 | 2022 |
| LiBRe: A Practical Bayesian Approach to Adversarial Detection Z Deng, X Yang, S Xu, H Su, J Zhu IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021, 2021 | 34 | 2021 |
| Cavs: An efficient runtime system for dynamic neural networks S Xu, H Zhang, G Neubig, W Dai, JK Kim, Z Deng, Q Ho, G Yang, EP Xing 2018 USENIX Annual Technical Conference (USENIX ATC 18), 937-950, 2018 | 22 | 2018 |
| Autosync: Learning to synchronize for data-parallel distributed deep learning H Zhang, Y Li, Z Deng, X Liang, L Carin, E Xing Advances in Neural Information Processing Systems 33, 906-917, 2020 | 18 | 2020 |
| NeuralEF: Deconstructing Kernels by Deep Neural Networks Z Deng, J Shi, J Zhu International Conference on Machine Learning (ICML) 162, https://proceedings
, 2022 | 10 | 2022 |
| BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian Fine-tuning Z Deng, J Zhu 14th Asian Conference on Machine Learning (ACML 2022), 2022 | 8* | 2022 |
| Neural eigenfunctions are structured representation learners Z Deng, J Shi, H Zhang, P Cui, C Lu, J Zhu arXiv preprint arXiv:2210.12637, 2022 | 6 | 2022 |
| Accelerated Linearized Laplace Approximation for Bayesian Deep Learning Z Deng, F Zhou, J Zhu Advances in Neural Information Processing Systems, 2022 | 5 | 2022 |
| Understanding and exploring the network with stochastic architectures Z Deng, Y Dong, S Zhang, J Zhu Advances in Neural Information Processing Systems 33, 14903-14914, 2020 | 4 | 2020 |
| Measuring uncertainty through bayesian learning of deep neural network structure Z Deng, Y Luo, J Zhu 2nd Workshop on Neural Architecture Search at ICLR 2021, 2021 | 3* | 2021 |
| Efficient inference for dynamic flexible interactions of neural populations F Zhou, Q Kong, Z Deng, J Kan, Y Zhang, C Feng, J Zhu Journal of Machine Learning Research, 2022 | 2 | 2022 |
| Accurate and reliable forecasting using stochastic differential equations P Cui, Z Deng, W Hu, J Zhu arXiv preprint arXiv:2103.15041, 2021 | 2 | 2021 |
| Learning Sample Difficulty from Pre-trained Models for Reliable Prediction P Cui, D Zhang, Z Deng, Y Dong, J Zhu arXiv preprint arXiv:2304.10127, 2023 | 1 | 2023 |
| On Calibrating Diffusion Probabilistic Models T Pang, C Lu, C Du, M Lin, S Yan, Z Deng arXiv preprint arXiv:2302.10688, 2023 | 1 | 2023 |
| Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation Z Deng, Y Luo International Conference on Computer Vision (ICCV), 2023 | 1 | 2023 |
Jun ZhuProfessor of Computer Science, Tsinghua UniversityPatvirtintas el. paštas mail.tsinghua.edu.cn
