Chi-Guhn Lee

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 2 papers; trusts 287 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Meta & Continual Learning — 17 citations into it; #334 of 1,041 by trust here, #827 by citations
• Reinforcement Learning — 11 citations into it
• Representation & Self-Supervised Learning — 9 citations into it; #1,254 of 3,145 by trust here, #2,516 by citations
• Radiology, Nuclear Medicine and Imaging — 6 citations into it

Whom Chi-Guhn Lee trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Spyros Gidaris
5. Yoshua Bengio
6. Diederik P. Kingma
7. Ross B. Girshick
8. Oriol Vinyals
9. Yann LeCun
10. Jia Deng

Who trusts Chi-Guhn Lee overall

1. Jaya Kalla
2. Mert Kilickaya
3. Do-Yeon Kim
4. Afra Feyza Akyürek
5. Pei-Hung Chen
6. Haeyong Kang
7. Guangyuan Shi
8. Song Tian
9. Yibo Yang
10. Soma Biswas

Papers from Chi-Guhn Lee's perspective

Ordered by how strongly the researchers Chi-Guhn Lee 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
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

• Incremental few-shot learning via vector quantization in deep embedded space (2021) — 121 citations
• Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach (2018) — 32 citations