Inyeop Lee

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 33 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it; #21,668 of 35,345 by trust here, #16,771 by citations
• Graph Learning — 28 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it; #13,467 of 21,575 by trust here, #11,286 by citations
• Model Architectures — 11 citations into it
• Molecular Biology — 9 citations into it
• Statistical and Nonlinear Physics — 5 citations into it; #5,663 of 10,127 by trust here, #3,284 by citations

Whom Inyeop Lee trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yann LeCun
4. Yoshua Bengio
5. Alex Krizhevsky
6. Oriol Vinyals
7. Ilya Sutskever
8. Kaiming He
9. Jimmy Ba
10. Max Welling

Who trusts Inyeop Lee overall

1. Filippo Maria Bianchi
2. Ekagra Ranjan
3. Lan Wei
4. Anton Tsitsulin
5. Davide Bacciu
6. Aneeshan Sain
7. Yingmin Wu
8. Seongjun Yun
9. Jie Zhou
10. Jianan Zhao

Papers from Inyeop Lee's perspective

Ordered by how strongly the researchers Inyeop 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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

• Self-Attention Graph Pooling (2019) — 1,340 citations