Junghyun Min
Artificial Intelligence · 2019–2020
A researcher in the Science corpus: 2 papers; trusts 41 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 53 citations into it; #14,205 of 35,345 by trust here, #12,387 by citations
- Natural Language Processing — 48 citations into it; #4,251 of 5,688 by trust here, #3,897 by citations
- Evaluation & Benchmarks — 18 citations into it; #2,193 of 3,147 by trust here, #2,166 by citations
- Computer Vision and Pattern Recognition — 15 citations into it; #15,841 of 21,575 by trust here, #13,269 by citations
- Large Language Models — 8 citations into it
Whom Junghyun Min trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- R. Thomas McCoy
- Jacob Devlin
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Yann LeCun
- Kaiming He
- Alex Graves
Who trusts Junghyun Min overall
- Joe Stacey
- Tal Linzen
- Yonatan Belinkov
- Victor Sanh
- Bohan Li
- Chuanrong Li
- Arian Hosseini
- Thomas Wolf
- Marek Rei
- Nafise Sadat Moosavi
Papers from Junghyun Min's perspective
Ordered by how strongly the researchers Junghyun Min trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition