Pengfei Chen

Artificial Intelligence · with Chen · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 375 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it; #17,691 of 35,345 by trust here, #7,062 by citations
• Computer Vision and Pattern Recognition — 91 citations into it; #14,532 of 21,575 by trust here, #6,089 by citations
• Optimization & Training — 43 citations into it; #2,006 of 2,601 by trust here, #1,804 by citations
• Model Architectures — 16 citations into it
• Safety, Robustness & Interpretability — 16 citations into it; #1,612 of 2,251 by trust here, #1,747 by citations
• Representation & Self-Supervised Learning — 10 citations into it
• Civil and Structural Engineering — 8 citations into it; #3,815 of 6,160 by trust here, #3,302 by citations
• Computational Mechanics — 7 citations into it

Whom Pengfei Chen trusts overall

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

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

Who trusts Pengfei Chen overall

1. Liang-Chieh Chen
2. Junnan Li
3. Curtis G. Northcutt
4. Hwanjun Song
5. Chris Dongjoo Kim
6. Geoff Pleiss
7. Sheng Liu
8. Lu Jiang
9. Swabha Swayamdipta
10. Steven C. H. Hoi

Papers from Pengfei Chen's perspective

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

Their papers

• Understanding Convolution for Semantic Segmentation (2017) — 1,908 citations
• 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
• Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise (2020) — 159 citations