S. Gene Kim

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it
• Optimization & Training — 8 citations into it

Whom S. Gene Kim trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Yoshua Bengio
5. Ilya Sutskever
6. Xavier Glorot
7. Yann LeCun
8. David E. Rumelhart
9. Nitish Srivastava
10. Jimmy Ba

Papers from S. Gene Kim's perspective

Ordered by how strongly the researchers S. Gene Kim 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. Gradient-based learning applied to document recognition
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. ImageNet classification with deep convolutional neural networks
6. Going deeper with convolutions
7. Dropout: a simple way to prevent neural networks from overfitting
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening (2019) — 596 citations
• High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks (2017) — 245 citations