Guangyong Chen(陈广勇)
Guangyong Chen(陈广勇)
Zhejiang University - Zhejiang Lab Hundred-Program Researcher
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
From noise modeling to blind image denoising
F Zhu, G Chen, PA Heng
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 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
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
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
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
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
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
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
Log hyperbolic cosine loss improves variational auto-encoder
P Chen, G Chen, S Zhang
A survey on generative diffusion model
H Cao, C Tan, Z Gao, G Chen, PA Heng, SZ Li
arXiv preprint arXiv:2209.02646, 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
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
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
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
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
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