| 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 | 281 | 2019 |
| 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 | 240 | 2015 |
| 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 | 219 | 2021 |
| 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 | 123 | 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 | 122 | 2017 |
| From noise modeling to blind image denoising F Zhu, G Chen, PA Heng Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 80 | 2016 |
| 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 | 65 | 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 28 (12), 4503-4514, 2021 | 61 | 2021 |
| 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 | 60 | 2019 |
| 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 | 52 | 2020 |
| 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 | 51 | 2019 |
| 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 | 44 | 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 | 44 | 2020 |
| Log hyperbolic cosine loss improves variational auto-encoder P Chen, G Chen, S Zhang | 33 | 2018 |
| A survey on generative diffusion model H Cao, C Tan, Z Gao, G Chen, PA Heng, SZ Li arXiv preprint arXiv:2209.02646, 2022 | 31 | 2022 |
| 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 | 26 | 2021 |
| 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 | 22 | 2017 |
| 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 | 21* | 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 | 19 | 2016 |
| 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 | 18 | 2021 |
Pheng Ann HengProfessor, Computer Science & Engineering, Chinese University of Hong KongVerified email at cse.cuhk.edu.hk
