Ko-Shin Chen

Graph Learning · 2018

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Graph Learning — 17 citations into it
• Molecular Biology — 10 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it

Whom Ko-Shin Chen trusts overall

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

1. Yann LeCun
2. Joan Bruna
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Thomas Kipf

Papers from Ko-Shin Chen's perspective

Ordered by how strongly the researchers Ko-Shin Chen trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Edge Attention-based Multi-Relational Graph Convolutional Networks (2018) — 96 citations