| Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models D Jiang, Z Wu, CY Hsieh, G Chen, B Liao, Z Wang, C Shen, D Cao, J Wu, ... Journal of cheminformatics 13, 1-23, 2021 | 538 | 2021 |
| Understanding and utilizing deep neural networks trained with noisy labels P Chen, BB Liao, G Chen, S Zhang International conference on machine learning, 1062-1070, 2019 | 451 | 2019 |
| A survey on generative diffusion models H Cao, C Tan, Z Gao, Y Xu, G Chen, PA Heng, SZ Li IEEE Transactions on Knowledge and Data Engineering, 2024 | 376 | 2024 |
| An Efficient Statistical Method for Image Noise Level Estimation G Chen, F Zhu, PA Heng International Conference on Computer Vision (ICCV), 2015, pp. 477-485, 2015 | 309 | 2015 |
| Qatten: A general framework for cooperative multiagent reinforcement learning Y Yang, J Hao, B Liao, K Shao, G Chen, W Liu, H Tang arXiv preprint arXiv:2002.03939, 2020 | 219 | 2020 |
| Cascaded feature network for semantic segmentation of RGB-D images D Lin, G Chen, D Cohen-Or, PA Heng, H Huang Proceedings of the IEEE international conference on computer vision, 1311-1319, 2017 | 164 | 2017 |
| Beyond class-conditional assumption: A primary attempt to combat instance-dependent label noise P Chen, J Ye, G Chen, J Zhao, PA Heng arXiv preprint arXiv:2012.05458, 2020 | 140 | 2020 |
| A rotation-invariant framework for deep point cloud analysis X Li, R Li, G Chen, CW Fu, D Cohen-Or, PA Heng IEEE transactions on visualization and computer graphics 28 (12), 4503-4514, 2021 | 113 | 2021 |
| RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions X Wang, Y Li, J Qiu, G Chen, H Liu, B Liao, CY Hsieh, X Yao Chemical Engineering Journal 420, 129845, 2021 | 108 | 2021 |
| Rethinking the usage of batch normalization and dropout in the training of deep neural networks G Chen, P Chen, Y Shi, CY Hsieh, B Liao, S Zhang arXiv preprint arXiv:1905.05928, 2019 | 100 | 2019 |
| From noise modeling to blind image denoising F Zhu, G Chen, PA Heng Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 92 | 2016 |
| Alchemy: A quantum chemistry dataset for benchmarking ai models G Chen, P Chen, CY Hsieh, CK Lee, B Liao, R Liao, W Liu, J Qiu, Q Sun, ... arXiv preprint arXiv:1906.09427, 2019 | 86 | 2019 |
| Spectral-based graph convolutional network for directed graphs Y Ma, J Hao, Y Yang, H Li, J Jin, G Chen arXiv preprint arXiv:1907.08990, 2019 | 84 | 2019 |
| Flattening sharpness for dynamic gradient projection memory benefits continual learning D Deng, G Chen, J Hao, Q Wang, PA Heng Advances in Neural Information Processing Systems 34, 18710-18721, 2021 | 81 | 2021 |
| Q-value path decomposition for deep multiagent reinforcement learning Y Yang, J Hao, G Chen, H Tang, Y Chen, Y Hu, C Fan, Z Wei International Conference on Machine Learning, 10706-10715, 2020 | 63 | 2020 |
| Deep learning-enabled orbital angular momentum-based information encryption transmission F Feng, J Hu, Z Guo, JA Gan, PF Chen, G Chen, C Min, X Yuan, ... ACS Photonics 9 (3), 820-829, 2022 | 61 | 2022 |
| Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method Z Wu, D Jiang, CY Hsieh, G Chen, B Liao, D Cao, T Hou Briefings in Bioinformatics 22 (5), bbab112, 2021 | 53 | 2021 |
| Balancing between accuracy and fairness for interactive recommendation with reinforcement learning W Liu, F Liu, R Tang, B Liao, G Chen, PA Heng Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia …, 2020 | 49* | 2020 |
| Log hyperbolic cosine loss improves variational auto-encoder P Chen, G Chen, S Zhang | 48 | 2019 |
| Efficient and accurate large library ligand docking with KarmaDock X Zhang, O Zhang, C Shen, W Qu, S Chen, H Cao, Y Kang, Z Wang, ... Nature Computational Science 3 (9), 789-804, 2023 | 45 | 2023 |
Pheng Ann HengChoh-Ming Li Professor of Computer Science and Engineering, The Chinese University of Hong Kong在 cse.cuhk.edu.hk 的電子郵件地址已通過驗證
