Donggyu Lee

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it; #10,083 of 35,345 by trust here, #12,761 by citations
• Computer Vision and Pattern Recognition — 10 citations into it; #6,999 of 21,575 by trust here, #8,831 by citations
• Optimization & Training — 7 citations into it; #1,041 of 2,601 by trust here, #1,195 by citations
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Donggyu Lee trusts overall

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

1. Diederik P. Kingma
2. Alex Graves
3. Geoffrey E. Hinton
4. Kaiming He
5. Alex Krizhevsky
6. Yoshua Bengio
7. Yann LeCun
8. Jimmy Ba
9. Max Welling
10. Radford M. Neal

Who trusts Donggyu Lee overall

1. Gyuhak Kim
2. Zixuan Ke
3. Bing Liu
4. Sangwon Jung
5. Qi Qin
6. Sungmin Cha
7. Jorge Armando Mendez Mendez
8. Rui Gao
9. Changnan Xiao
10. Hongjoon Ahn

Papers from Donggyu Lee's perspective

Ordered by how strongly the researchers Donggyu 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Uncertainty-based Continual Learning with Adaptive Regularization (2019) — 274 citations
• Continual Learning in the Presence of Spurious Correlations: Analyses and a Simple Baseline (2024) — 9 citations