Dokook Choe

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 25 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #19,065 of 35,345 by trust here, #10,065 by citations
• Natural Language Processing — 33 citations into it; #3,307 of 5,688 by trust here, #2,220 by citations
• Model Architectures — 28 citations into it
• Optimization & Training — 10 citations into it

Whom Dokook Choe trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Sepp Hochreiter
5. Noam Shazeer
6. Ilya Sutskever
7. Tomas Mikolov
8. Jimmy Ba
9. Alex Krizhevsky
10. J. Urgen Schmidhuber

Who trusts Dokook Choe overall

1. Zihang Dai
2. Derya Soydaner
3. Jingjing Xu
4. Uri Shaham
5. Kazuki Irie
6. Alexei Baevski
7. Albert Zeyer
8. Sainbayar Sukhbaatar
9. Zhilin Yang
10. Francisca Adoma Acheampong

Papers from Dokook Choe's perspective

Ordered by how strongly the researchers Dokook Choe 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Character-Level Language Modeling with Deeper Self-Attention (2018) — 421 citations
• Bridging the Gap for Tokenizer-Free Language Models (2019) — 27 citations