Youngjin Kwon

Artificial Intelligence · 2021

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 — 24 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Optimization & Training — 11 citations into it

Whom Youngjin Kwon trusts overall

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

1. Diederik P. Kingma
2. Ilya Sutskever
3. Alec Radford
4. Geoffrey E. Hinton
5. Kaiming He
6. Alex Krizhevsky
7. Yoshua Bengio

Papers from Youngjin Kwon's perspective

Ordered by how strongly the researchers Youngjin 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Zico: Efficient GPU Memory Sharing for Concurrent DNN Training (2021) — 63 citations