Hong-Jun Yoon

Artificial Intelligence · 2016–2018

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

Subfields

• Artificial Intelligence — 10 citations into it; #27,682 of 35,345 by trust here, #33,294 by citations

Whom Hong-Jun Yoon trusts overall

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

1. Yoon Kim
2. Diederik P. Kingma
3. Gerard A. Salton
4. Jimmy Ba
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Pavlo Molchanov
8. Alex Krizhevsky
9. Yann LeCun
10. Ilya Sutskever

Who trusts Hong-Jun Yoon overall

1. Cao Xiao
2. Ting-Wu Chin
3. Edward Choi
4. Cha Zhang

Papers from Hong-Jun Yoon's perspective

Ordered by how strongly the researchers Hong-Jun Yoon trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Multi-task Deep Neural Networks for Automated Extraction of Primary Site and Laterality Information from Cancer Pathology Reports (2016) — 33 citations
• Filter pruning of Convolutional Neural Networks for text classification: A case study of cancer pathology report comprehension (2018) — 16 citations