Hakmin Lee

Computer Vision and Pattern Recognition · 2018

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 6 citations into it

Whom Hakmin Lee trusts overall

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

1. Karen Simonyan
2. Olaf Ronneberger
3. Andrew Zisserman

Papers from Hakmin Lee's perspective

Ordered by how strongly the researchers Hakmin Lee trusts endorse each paper. Same corpus, a different order for every researcher.

1. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
2. GENERATIVE ADVERSARIAL NETS
3. ImageNet classification with deep convolutional neural networks
4. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Long Short-Term Memory
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• ICADx: interpretable computer aided diagnosis of breast masses (2018) — 31 citations