Jaehyun Jun

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 158 researchers and is trusted by 417 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #2,252 of 35,345 by trust here, #2,604 by citations
• Computer Vision and Pattern Recognition — 38 citations into it; #2,059 of 21,575 by trust here, #1,929 by citations
• Multimodal Learning — 15 citations into it
• Optimization & Training — 12 citations into it
• Model Architectures — 9 citations into it; #321 of 2,278 by trust here, #635 by citations
• Natural Language Processing — 7 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it
• Representation & Self-Supervised Learning — 5 citations into it; #745 of 3,145 by trust here, #518 by citations

Whom Jaehyun Jun trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Kaiming He
5. Alex Krizhevsky
6. Ross B. Girshick
7. Shaoqing Ren
8. Yann LeCun
9. Ilya Sutskever
10. Vinod Nair

Who trusts Jaehyun Jun overall

1. Rahaf Aljundi
2. Linjie Li
3. Sedigheh Eslami
4. Pan Lu
5. Kushal Kafle
6. Liunian Harold Li
7. Marc Masana
8. Zhe Gan
9. Junnan Li
10. Robik Shrestha

Papers from Jaehyun Jun's perspective

Ordered by how strongly the researchers Jaehyun Jun 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. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Bilinear Attention Networks (2018) — 1,059 citations
• Overcoming Catastrophic Forgetting by Incremental Moment Matching (2017) — 738 citations