Yo Joong Choe

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 3 papers; trusts 72 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #27,161 of 35,345 by trust here, #25,809 by citations
• Natural Language Processing — 48 citations into it; #4,448 of 5,688 by trust here, #4,983 by citations
• Computer Vision and Pattern Recognition — 23 citations into it; #16,662 of 21,575 by trust here, #18,266 by citations
• Representation & Self-Supervised Learning — 20 citations into it
• Large Language Models — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Yo Joong Choe trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Jimmy Ba
4. Yoshua Bengio
5. Kaiming He
6. Ilya Sutskever
7. Rico Sennrich
8. Alex Krizhevsky
9. Yann LeCun
10. Tomas Mikolov

Who trusts Yo Joong Choe overall

1. Max White
2. Zhaohong Wan
3. Yu Wang
4. Christopher Bryant
5. Yu Wang
6. Alla Rozovskaya
7. Xiaojun Wan
8. Mariano Felice
9. Yuelin Wang
10. Yuelin Wang

Papers from Yo Joong Choe's perspective

Ordered by how strongly the researchers Yo Joong 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. 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation (2019) — 366 citations
• The Linear Representation Hypothesis and the Geometry of Large Language Models (2023) — 626 citations
• A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning (2019) — 89 citations