Yelin Kim

Artificial Intelligence · 2013–2015

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 22 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Signal Processing — 13 citations into it; #3,675 of 7,572 by trust here, #2,664 by citations
• Experimental and Cognitive Psychology — 8 citations into it; #3,684 of 9,731 by trust here, #3,670 by citations

Whom Yelin Kim trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Honglak Lee
4. Alex Krizhevsky
5. Chi-Chun Lee
6. Andrew Y. Ng
7. Yann LeCun
8. Ilya Sutskever
9. Garimella S. V. S. Sivaram
10. Diederik P. Kingma

Who trusts Yelin Kim overall

1. Jinkyu Lee
2. Tadas Baltrusaitis
3. Jinpeng Li
4. Zubair Muhammad Fadlullah
5. Youngjin Yoo
6. Ivan J. Tashev
7. Lucy R. Chai
8. Chaitanya Ahuja
9. Zhaoxiang Zhang
10. Fengxiao Tang

Papers from Yelin Kim's perspective

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

Their papers

• Modeling transition patterns between events for temporal human action segmentation and classification (2015) — 16 citations
• Deep learning for robust feature generation in audiovisual emotion recognition (2013) — 409 citations