Janghyeon Lee

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 3 papers; trusts 366 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 79 citations into it; #23,132 of 35,345 by trust here, #20,836 by citations
• Computer Vision and Pattern Recognition — 69 citations into it
• Optimization & Training — 31 citations into it; #1,986 of 2,601 by trust here, #2,052 by citations
• Representation & Self-Supervised Learning — 27 citations into it
• Model Architectures — 18 citations into it
• Meta & Continual Learning — 13 citations into it; #732 of 1,041 by trust here, #756 by citations
• Statistical and Nonlinear Physics — 9 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Janghyeon Lee trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. James Martens
7. Diederik P. Kingma
8. Sergey Ioffe
9. Karen Simonyan
10. X. Zhang

Who trusts Janghyeon Lee overall

1. Liyuan Wang
2. Da-Wei Zhou
3. Yajing Kong
4. Hyounguk Shon
5. Prakhar Kaushik
6. Xingxing Zhang
7. Wenpeng Hu
8. Jeffrey O. Zhang
9. Qiwen Wang
10. Marc Masana

Papers from Janghyeon Lee's perspective

Ordered by how strongly the researchers Janghyeon 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Continual Learning With Extended Kronecker-Factored Approximate Curvature (2020) — 72 citations
• Residual Continual Learning (2020) — 25 citations
• DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning (2022) — 20 citations