Wenling Shang

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 6 papers; trusts 428 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 146 citations into it; #11,757 of 35,345 by trust here, #8,999 by citations
• Reinforcement Learning — 112 citations into it
• Computer Vision and Pattern Recognition — 73 citations into it; #8,982 of 21,575 by trust here, #9,435 by citations
• Model Architectures — 23 citations into it; #1,012 of 2,278 by trust here, #1,285 by citations
• Optimization & Training — 19 citations into it
• Generative Models — 17 citations into it
• Management Science and Operations Research — 17 citations into it
• Representation & Self-Supervised Learning — 17 citations into it

Whom Wenling Shang trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Richard S. Sutton
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Alex Graves
9. Kaiming He
10. Ruslan Salakhutdinov

Who trusts Wenling Shang overall

1. Yu Cheng
2. Douwe van der Wal
3. Jeff Donahue
4. Alexander Kolesnikov
5. Taco Cohen
6. Shan Li
7. David Balduzzi
8. Audrunas Gruslys
9. Zhong-Qiu Zhao
10. Max Welling

Papers from Wenling Shang's perspective

Ordered by how strongly the researchers Wenling Shang 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units (2016) — 530 citations
• ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games (2017) — 131 citations
• Stochastic Activation Actor Critic Methods (2019) — 4 citations
• Learning World Graphs to Accelerate Hierarchical Reinforcement Learning (2019) — 23 citations
• Advantage Actor-Critic Methods for CarRacing (2018)
• Improved Multimodal Deep Learning with Variation of Information (2014) — 191 citations