| 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 | 200 | 2015 |
| 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 | 178 | 2019 |
| 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 | 100 | 2017 |
| 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), 1-23, 2021 | 80 | 2021 |
| From noise modeling to blind image denoising F Zhu, G Chen, PA Heng Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 70 | 2016 |
| 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 | 66 | 2020 |
| 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 | 50 | 2019 |
| 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 | 43 | 2019 |
| 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, 2021 | 31 | 2021 |
| 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 | 27 | 2019 |
| 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 | 24 | 2020 |
| Log hyperbolic cosine loss improves variational auto-encoder P Chen, G Chen, S Zhang | 22 | 2018 |
| 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 | 21 | 2020 |
| Efficient and Robust Emergence of Norms through Heuristic Collective Learning J Hao, JS Sun, G Chen, Z Wang, C Yu, Z Ming ACM Transactions on Autonomous and Adaptive Systems (TAAS) 12 (4), Article …, 2017 | 21 | 2017 |
| 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 | 20 | 2021 |
| Blind image denoising via dependent Dirichlet process tree F Zhu, G Chen, J Hao, PA Heng IEEE transactions on pattern analysis and machine intelligence 39 (8), 1518-1531, 2016 | 18 | 2016 |
| Robustness of accuracy metric and its inspirations in learning with noisy labels P Chen, J Ye, G Chen, J Zhao, PA Heng Proceedings of the AAAI Conference on Artificial Intelligence 35 (13), 11451 …, 2021 | 14 | 2021 |
| Balancing between accuracy and fairness for interactive recommendation with reinforcement learning W Liu, F Liu, R Tang, B Liao, G Chen, PA Heng Pacific-asia conference on knowledge discovery and data mining, 155-167, 2020 | 10 | 2020 |
| Utilizing edge features in graph neural networks via variational information maximization P Chen, W Liu, CY Hsieh, G Chen, S Zhang arXiv preprint arXiv:1906.05488, 2019 | 9 | 2019 |
| 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 | 8 | 2021 |
Pheng Ann HengProfessor, Computer Science & Engineering, Chinese University of Hong Kong在 cse.cuhk.edu.hk 的電子郵件地址已通過驗證
