Chulhee Yun

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 44 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #26,790 of 35,345 by trust here, #12,761 by citations
• Model Architectures — 27 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Optimization & Training — 18 citations into it
• Computational Mechanics — 10 citations into it
• Statistics and Probability — 8 citations into it
• Statistical Learning Theory — 6 citations into it

Whom Chulhee Yun trusts overall

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

1. Geoffrey E. Hinton
2. Ilya Sutskever
3. Yoshua Bengio
4. Alex Krizhevsky
5. Alec Radford
6. Kaiming He
7. Ashish Vaswani
8. Diederik P. Kingma
9. Kenji Kawaguchi
10. Yann LeCun

Who trusts Chulhee Yun overall

1. Julian Martin Eisenschlos
2. Quynh N. Nguyen
3. Maharshi Gor
4. Shashank Rajput
5. Manzil Zaheer
6. Yaodong Yang
7. Leo Liberti
8. Thomas Müller
9. Shuhao Cao
10. Lam M. Nguyen

Papers from Chulhee Yun's perspective

Ordered by how strongly the researchers Chulhee Yun 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Are Transformers universal approximators of sequence-to-sequence functions? (2019) — 533 citations
• $O(n)$ Connections are Expressive Enough: Universal Approximability of Sparse Transformers (2020) — 108 citations
• Global optimality conditions for deep neural networks (2017) — 128 citations
• SGD with shuffling: optimal rates without component convexity and large epoch requirements (2020) — 77 citations
• Minimax Bounds on Stochastic Batched Convex Optimization (2018) — 27 citations