Namhoon Lee

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 6 papers; trusts 543 researchers and is trusted by 71 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 139 citations into it; #3,713 of 21,575 by trust here, #3,141 by citations
• Artificial Intelligence — 90 citations into it; #5,289 of 35,345 by trust here, #4,656 by citations
• Efficiency & Compression — 23 citations into it; #324 of 1,264 by trust here, #573 by citations
• Model Architectures — 23 citations into it
• Optimization & Training — 17 citations into it
• Natural Language Processing — 13 citations into it
• Reinforcement Learning — 12 citations into it; #1,571 of 2,242 by trust here, #992 by citations
• Generative Models — 10 citations into it

Whom Namhoon Lee trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Kaiming He
7. Karen Simonyan
8. Ilya Sutskever
9. Andrew Zisserman
10. Jimmy Ba

Who trusts Namhoon Lee overall

1. Jiahui Yu
2. Yang He
3. Nicholas Rhinehart
4. Vivek Ramanujan
5. Utku Evci
6. Nachiket Deo
7. Jonathan Frankle
8. Thomas S. Huang
9. Tim Dettmers
10. Eugene Lee

Papers from Namhoon Lee's perspective

Ordered by how strongly the researchers Namhoon Lee 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents (2017) — 1,103 citations
• SNIP: Single-shot Network Pruning based on Connection Sensitivity (2018) — 1,499 citations
• Forecasting Interactive Dynamics of Pedestrians with Fictitious Play (2016) — 166 citations
• The Sixth Visual Object Tracking VOT2018 Challenge Results (2018) — 797 citations
• A Signal Propagation Perspective for Pruning Neural Networks at Initialization (2019) — 174 citations
• Learn To Pay Attention (2018) — 474 citations