Wei-Han Lee

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 45 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Efficiency & Compression — 24 citations into it
• Federated & Distributed Learning — 13 citations into it

Whom Wei-Han Lee trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Yoshua Bengio
6. Kaiming He
7. Andrew Zisserman
8. Diederik P. Kingma
9. L. Eon Bottou
10. Jia Deng

Papers from Wei-Han Lee's perspective

Ordered by how strongly the researchers Wei-Han Lee 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. ImageNet classification with deep convolutional neural networks
4. ImageNet: A large-scale hierarchical image database
5. Learning Multiple Layers of Features from Tiny Images
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Distilling the Knowledge in a Neural Network (arXiv.org)

Their papers

• Model Pruning Enables Efficient Federated Learning on Edge Devices (2019) — 650 citations