Fei Chen

Artificial Intelligence · with Li · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 75 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 89 citations into it; #8,426 of 35,345 by trust here, #5,565 by citations
• Computer Vision and Pattern Recognition — 40 citations into it; #6,354 of 21,575 by trust here, #5,082 by citations
• Federated & Distributed Learning — 28 citations into it; #182 of 319 by trust here, #132 by citations
• Representation & Self-Supervised Learning — 22 citations into it
• Optimization & Training — 17 citations into it
• Reinforcement Learning — 15 citations into it
• Meta & Continual Learning — 13 citations into it; #194 of 1,041 by trust here, #99 by citations
• Model Architectures — 6 citations into it

Whom Fei Chen trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yann LeCun
4. Chelsea Finn
5. Yoshua Bengio
6. Diederik P. Kingma
7. Oriol Vinyals
8. Pieter Abbeel
9. Ilya Sutskever
10. Hugo Larochelle

Who trusts Fei Chen overall

1. Alireza Fallah
2. Mikhail Khodak
3. Canh T. Dinh
4. Aryan Mokhtari
5. Jaehoon Oh
6. Qinbin Li
7. Yutao Huang
8. Alessio Zappone
9. Han-Jia Ye
10. Marvin Zhang

Papers from Fei Chen's perspective

Ordered by how strongly the researchers Fei 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Meta-SGD: Learning to Learn Quickly for Few Shot Learning (2017) — 1,259 citations
• Federated Meta-Learning with Fast Convergence and Efficient Communication (2018) — 475 citations
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data (2021) — 483 citations
• Federated Meta-Learning for Recommendation (2018) — 140 citations