Jun S. Song

Molecular Biology · 2017

A researcher in the Science corpus: 1 paper; trusts 22 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 10 citations into it
• Artificial Intelligence — 5 citations into it

Whom Jun S. Song trusts overall

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

1. Karen Simonyan
2. Timothy L. Bailey
3. Nitish Srivastava
4. Geoffrey E. Hinton
5. Avanti Shrikumar
6. Gavin E. Crooks
7. Haoyang Zeng
8. Alex Krizhevsky
9. Andrea Vedaldi
10. Andrew Zisserman

Papers from Jun S. Song's perspective

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

Their papers

• Maximum entropy methods for extracting the learned features of deep neural networks (2017) — 41 citations