Yeong-Dae Kwon

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 17 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Reinforcement Learning — 7 citations into it

Whom Yeong-Dae Kwon trusts overall

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

1. Ronald J. Williams
2. Ilya Sutskever
3. Alex Krizhevsky
4. Christian Szegedy
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Dzmitry Bahdanau
8. Oriol Vinyals
9. Diederik P. Kingma
10. Wouter Kool

Papers from Yeong-Dae Kwon's perspective

Ordered by how strongly the researchers Yeong-Dae Kwon 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• POMO: Policy Optimization with Multiple Optima for Reinforcement Learning (2020) — 645 citations