Seongjun Yun

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 3 papers; trusts 69 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #31,949 of 35,345 by trust here, #11,624 by citations
• Graph Learning — 35 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it
• Statistical and Nonlinear Physics — 16 citations into it
• Natural Language Processing — 15 citations into it; #5,457 of 5,688 by trust here, #3,687 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Molecular Biology — 10 citations into it
• Information Retrieval & Search — 8 citations into it; #988 of 1,022 by trust here, #563 by citations

Whom Seongjun Yun trusts overall

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

1. Thomas Kipf
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Max Welling
5. Yoshua Bengio
6. Yann LeCun
7. Alex Krizhevsky
8. Ilya Sutskever
9. Kaiming He
10. Oriol Vinyals

Who trusts Seongjun Yun overall

1. Yuhui Ding
2. Shuaicheng Zhang
3. Lyle Regenwetter
4. Yu Chen
5. Jianan Zhao
6. Jinhyuk Lee
7. Johannes von Oswald
8. Gautier Izacard
9. Youngjin Jang
10. Xiao Wang

Papers from Seongjun Yun's perspective

Ordered by how strongly the researchers Seongjun Yun 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering (2018) — 90 citations
• Graph Transformer Networks (2019) — 1,382 citations
• DyGRAIN: An Incremental Learning Framework for Dynamic Graphs (2022) — 39 citations