| Noise optimization in artificial neural networks L Xiao, Z Zhang, K Huang, J Jiang, Y Peng IEEE Transactions on Automation Science and Engineering, 2024 | 14 | 2024 |
| Quantile-based policy optimization for reinforcement learning J Jiang, Y Peng, J Hu 2022 Winter Simulation Conference (WSC), 2712-2723, 2022 | 5 | 2022 |
| One forward is enough for neural network training via likelihood ratio method J Jiang, Z Zhang, C Xu, Z Yu, Y Peng arXiv preprint arXiv:2305.08960, 2023 | 3 | 2023 |
| Training neural networks without backpropagation: A deeper dive into the likelihood ratio method J Jiang, Z Zhang, C Xu, Z Yu, Y Peng arXiv preprint arXiv:2305.08960, 2023 | 3 | 2023 |
| A novel noise injection-based training scheme for better model robustness Z Zhang, J Jiang, M Chen, Z Wang, Y Peng, Z Yu arXiv preprint arXiv:2302.10802, 2023 | 2 | 2023 |
| Forward Learning for Gradient-based Black-box Saliency Map Generation Z Zhang, M Feng, J Jiang, R Zhu, Y Peng, C Xu arXiv preprint arXiv:2403.15603, 2024 | 1 | 2024 |
| Quantile-based deep reinforcement learning using two-timescale policy gradient algorithms J Jiang, J Hu, Y Peng arXiv preprint arXiv:2305.07248, 2023 | 1 | 2023 |
| Approximated Likelihood Ratio: A Forward-Only and Parallel Framework for Boosting Neural Network Training Z Zhang, J Jiang, Z Liu, S Liang, Y Peng, C Xu arXiv preprint arXiv:2403.12320, 2024 | | 2024 |
| Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode J Jiang, X Liu, T Ren, Q Wang, Y Zheng, Y Du, Y Peng, C Zhang arXiv preprint arXiv:2403.00318, 2024 | | 2024 |
| RiskMiner: Discovering Formulaic Alphas via Risk Seeking Monte Carlo Tree Search T Ren, R Zhou, J Jiang, J Liang, Q Wang, Y Peng arXiv preprint arXiv:2402.07080, 2024 | | 2024 |
Yijie PengPeking UniversityVerified email at pku.edu.cn
