| Cluster Alignment with a Teacher for Unsupervised Domain Adaptation Z Deng, Y Luo, J Zhu IEEE International Conference on Computer Vision 2019, 2019 | 178 | 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 | 69 | 2020 |
| Batch Virtual Adversarial Training for Graph Convolutional Networks Z Deng, Y Dong, J Zhu ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data, 2019 | 68 | 2019 |
| 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 | 62 | 2017 |
| 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 | 39 | 2021 |
| Exploring memorization in adversarial training Y Dong, K Xu, X Yang, T Pang, Z Deng, H Su, J Zhu ICLR 2022, 2022 | 26 | 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 | 23 | 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 | 18 | 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 | 14 | 2020 |
| NeuralEF: Deconstructing Kernels by Deep Neural Networks Z Deng, J Shi, J Zhu International Conference on Machine Learning (ICML) 162, https://proceedings …, 2022 | 6 | 2022 |
| BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian Fine-tuning Z Deng, J Zhu 14th Asian Conference on Machine Learning (ACML 2022), 2022 | 6* | 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 | 3 | 2022 |
| 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 |
| 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 | 3 | 2020 |
| Accelerated Linearized Laplace Approximation for Bayesian Deep Learning Z Deng, F Zhou, J Zhu Advances in Neural Information Processing Systems, 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 |
| On Calibrating Diffusion Probabilistic Models T Pang, C Lu, C Du, M Lin, S Yan, Z Deng arXiv preprint arXiv:2302.10688, 2023 | | 2023 |
| Deep Ensemble as a Gaussian Process Approximate Posterior Z Deng, F Zhou, J Chen, G Wu, J Zhu arXiv preprint arXiv:2205.00163, 2022 | | 2022 |
| Confidence-based Reliable Learning under Dual Noises P Cui, Y Yue, Z Deng, J Zhu Advances in Neural Information Processing Systems, 2022 | | 2022 |
| 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 23 (211), 1-49, 2022 | | 2022 |
Jun ZhuProfessor of Computer Science, Tsinghua UniversityVerified email at mail.tsinghua.edu.cn
