Seunghyun Yoon

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 4 papers; trusts 115 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it; #16,360 of 35,345 by trust here, #25,809 by citations
• Natural Language Processing — 57 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it; #14,479 of 21,575 by trust here, #20,271 by citations
• Signal Processing — 13 citations into it
• Model Architectures — 11 citations into it
• Information Systems — 10 citations into it; #4,640 of 8,771 by trust here, #7,107 by citations
• Experimental and Cognitive Psychology — 9 citations into it

Whom Seunghyun Yoon trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Richard Socher
5. Alex Krizhevsky
6. Jeffrey Pennington
7. Kyunghyun Cho
8. Sepp Hochreiter
9. Ilya Sutskever
10. Jimmy Ba

Who trusts Seunghyun Yoon overall

1. Pushkar Mishra
2. Younghun Lee
3. Taiba Maijd Wani
4. Helen Yannakoudakis
5. Yang Gao
6. Ekaterina Shutova
7. Steve Durairaj Swamy
8. Teddy Surya Gunawan
9. Weiyi Zheng

Papers from Seunghyun Yoon's perspective

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

Their papers

• Factual Error Correction for Abstractive Summaries Using Entity Retrieval (2022) — 15 citations
• Multimodal Speech Emotion Recognition Using Audio and Text (2018) — 352 citations
• Comparative Studies of Detecting Abusive Language on Twitter (2018) — 81 citations
• Learning to Rank Question-Answer Pairs Using Hierarchical Recurrent Encoder with Latent Topic Clustering (2017) — 35 citations