Jaeho Lee

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 182 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it; #6,966 of 35,345 by trust here, #11,750 by citations
• Computer Vision and Pattern Recognition — 57 citations into it; #5,254 of 21,575 by trust here, #8,205 by citations
• Efficiency & Compression — 27 citations into it
• Representation & Self-Supervised Learning — 26 citations into it; #1,149 of 3,145 by trust here, #1,781 by citations
• Optimization & Training — 13 citations into it
• Meta & Continual Learning — 12 citations into it; #268 of 1,041 by trust here, #502 by citations
• Model Architectures — 9 citations into it

Whom Jaeho Lee trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Geoffrey E. Hinton
4. Kaiming He
5. Diederik P. Kingma
6. Karen Simonyan
7. Yoshua Bengio
8. Yuhai Wu
9. Cédric Villani
10. Ilya Sutskever

Who trusts Jaeho Lee overall

1. Wouter M. Kouw
2. Soroosh Shafieezadeh-Abadeh
3. Mehryar Mohri
4. Gyuhak Kim
5. Marco Loog
6. Riccardo Volpi
7. John C. Duchi
8. Le Yu
9. Zifeng Wang
10. D. Kühn

Papers from Jaeho Lee's perspective

Ordered by how strongly the researchers Jaeho Lee trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Co2L: Contrastive Continual Learning (2021) — 408 citations
• Minimax Statistical Learning and Domain Adaptation with Wasserstein Distances (2017) — 21 citations
• Learning from Failure: De-biasing Classifier from Biased Classifier (2020) — 416 citations
• Layer-adaptive Sparsity for the Magnitude-based Pruning (2020) — 354 citations