Jinghui Chen

Artificial Intelligence · with Lee · 2017–2022

A researcher in the Science corpus: 3 papers; trusts 86 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 66 citations into it; #19,578 of 35,345 by trust here, #13,353 by citations
• Optimization & Training — 24 citations into it; #2,238 of 2,601 by trust here, #1,638 by citations
• Computer Vision and Pattern Recognition — 23 citations into it; #17,259 of 21,575 by trust here, #12,419 by citations
• Natural Language Processing — 20 citations into it
• Safety, Robustness & Interpretability — 12 citations into it
• Management Science and Operations Research — 11 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Statistics and Probability — 8 citations into it

Whom Jinghui Chen trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Matthew D. Zeiler
5. Karen Simonyan
6. Yoshua Bengio
7. Kaiming He
8. Sepp Hochreiter
9. Ilya Sutskever
10. Yann LeCun

Who trusts Jinghui Chen overall

1. Guansong Pang
2. Liron Bergman
3. Qianqian Tong
4. Lukas Ruff
5. Sai Praneeth Karimireddy
6. Seyed Iman Mirzadeh
7. Xiangyi Chen
8. Xiaoyi Gu
9. Xinyi Chen
10. Hongzuo Xu

Papers from Jinghui Chen's perspective

Ordered by how strongly the researchers Jinghui 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks (2018) — 222 citations
• Outlier Detection with Autoencoder Ensembles (2017) — 455 citations
• Do Language Models Plagiarize? (2022) — 115 citations