Chen Gong

Artificial Intelligence · 2014–2023

A researcher in the Science corpus: 10 papers; trusts 521 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 164 citations into it
• Computer Vision and Pattern Recognition — 146 citations into it; #17,624 of 21,575 by trust here, #15,955 by citations
• Representation & Self-Supervised Learning — 44 citations into it
• Optimization & Training — 36 citations into it
• Media Technology — 34 citations into it
• Graph Learning — 30 citations into it
• Statistical and Nonlinear Physics — 21 citations into it
• Statistical Learning Theory — 19 citations into it

Whom Chen Gong trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Diederik P. Kingma
6. Yann LeCun
7. Karen Simonyan
8. Ilya Sutskever
9. Thorsten Joachims
10. Andrew Zisserman

Who trusts Chen Gong overall

1. Yang Fan
2. Serban Stan
3. Yuan Gao
4. Xiaojun Chang
5. Jianping Gou
6. Fei Tian

Papers from Chen Gong's perspective

Ordered by how strongly the researchers Chen Gong 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Label Propagation via Teaching-to-Learn and Learning-to-Teach (2017) — 136 citations
• Are Anchor Points Really Indispensable in Label-Noise Learning? (2019) — 461 citations
• Cross-level Feature Aggregation Network for Polyp Segmentation (2023) — 295 citations
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning (2021) — 488 citations
• Robust early-learning: Hindering the memorization of noisy labels (2021) — 338 citations
• Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification (2019) — 463 citations
• Teaching Semi-Supervised Classifier via Generalized Distillation (2018) — 28 citations
• Importance-Aware Semantic Segmentation for Autonomous Driving System (2017) — 41 citations
• Deformed Graph Laplacian for Semisupervised Learning (2015) — 124 citations
• Signed Laplacian Embedding for Supervised Dimension Reduction (2014) — 17 citations