Dong-Hyun Lee

Artificial Intelligence · 2013–2015

A researcher in the Science corpus: 4 papers; trusts 151 researchers and is trusted by 393 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #10,829 of 35,345 by trust here, #7,413 by citations
• Computer Vision and Pattern Recognition — 33 citations into it; #11,744 of 21,575 by trust here, #7,249 by citations
• Representation & Self-Supervised Learning — 26 citations into it; #65 of 3,145 by trust here, #456 by citations
• Model Architectures — 13 citations into it
• Optimization & Training — 13 citations into it; #79 of 2,601 by trust here, #340 by citations
• Probabilistic & Bayesian Methods — 12 citations into it
• Generative Models — 9 citations into it
• Cognitive Neuroscience — 6 citations into it; #4,624 of 10,852 by trust here, #4,443 by citations

Whom Dong-Hyun Lee trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Pascal Vincent
4. Hugo Larochelle
5. Alex Krizhevsky
6. Leo Breiman
7. Salah Rifai
8. Ruslan Salakhutdinov
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Dong-Hyun Lee overall

1. Jian Liang
2. Qizhe Xie
3. Mahmoud Assran
4. Yoshua Bengio
5. Yassine Ouali
6. Yanbei Chen
7. Yi Yang
8. Quoc V. Le
9. Kihyuk Sohn
10. Dapeng Hu

Papers from Dong-Hyun Lee's perspective

Ordered by how strongly the researchers Dong-Hyun 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Challenges in representation learning: A report on three machine learning contests (2013) — 2,020 citations
• Difference Target Propagation (2014) — 422 citations
• Towards Biologically Plausible Deep Learning (2015) — 383 citations
• Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks (2013) — 4,728 citations