Guangyong Chen

Artificial Intelligence · with Chen · 2019–2021

A researcher in the Science corpus: 6 papers; trusts 241 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 148 citations into it; #19,632 of 35,345 by trust here, #14,181 by citations
• Computer Vision and Pattern Recognition — 79 citations into it; #16,694 of 21,575 by trust here, #19,310 by citations
• Optimization & Training — 51 citations into it; #1,679 of 2,601 by trust here, #1,624 by citations
• Reinforcement Learning — 40 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Safety, Robustness & Interpretability — 20 citations into it; #1,613 of 2,251 by trust here, #1,747 by citations
• Scaling & Learning Theory — 10 citations into it
• Model Architectures — 9 citations into it

Whom Guangyong Chen trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Yann LeCun
6. Diederik P. Kingma
7. Ilya Sutskever
8. Christian Szegedy
9. Ross B. Girshick
10. Karen Simonyan

Who trusts Guangyong Chen overall

1. Liyuan Wang
2. Junnan Li
3. Curtis G. Northcutt
4. Xingxing Zhang
5. Pengfei Chen
6. Hwanjun Song

Papers from Guangyong Chen's perspective

Ordered by how strongly the researchers Guangyong Chen trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels (2019) — 460 citations
• Noise against noise: stochastic label noise helps combat inherent label noise (2021) — 46 citations
• Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning (2021) — 110 citations
• Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise (2020) — 159 citations
• Q-value Path Decomposition for Deep Multiagent Reinforcement Learning (2020) — 65 citations
• Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning (2020) — 236 citations