Linnan Wang

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 281 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 110 citations into it; #16,747 of 35,345 by trust here, #18,186 by citations
• Computer Vision and Pattern Recognition — 92 citations into it; #10,628 of 21,575 by trust here, #12,172 by citations
• Model Architectures — 42 citations into it
• Neural Architecture Search & AutoML — 35 citations into it; #147 of 283 by trust here, #195 by citations
• Optimization & Training — 25 citations into it
• Reinforcement Learning — 18 citations into it
• Management Science and Operations Research — 15 citations into it
• Evaluation & Benchmarks — 13 citations into it

Whom Linnan Wang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Frank Hutter
4. Barret Zoph
5. Yoshua Bengio
6. Kaiming He
7. Quoc V. Le
8. Diederik P. Kingma
9. Yann LeCun
10. Ilya Sutskever

Who trusts Linnan Wang overall

1. Colin White
2. Han Shi
3. Yash Savani
4. Sam Nolen
5. Jie Ren
6. Willie Neiswanger
7. Chepuri Shri Krishna
8. Vu Nguyen
9. Renjie Pi
10. Chen Wei

Papers from Linnan Wang's perspective

Ordered by how strongly the researchers Linnan Wang 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. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search (2019) — 100 citations
• Sample-Efficient Neural Architecture Search by Learning Action Space (2019) — 48 citations
• Neural Architecture Search Using Deep Neural Networks and Monte Carlo Tree Search (2018) — 56 citations
• Superneurons: dynamic GPU memory management for training deep neural networks (2018) — 294 citations