Wenyuan Zeng

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 261 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 103 citations into it; #10,448 of 35,345 by trust here, #5,834 by citations
• Computer Vision and Pattern Recognition — 82 citations into it; #20,359 of 21,575 by trust here, #8,021 by citations
• Natural Language Processing — 39 citations into it
• Automotive Engineering — 25 citations into it; #2,742 of 3,081 by trust here, #1,026 by citations
• Optimization & Training — 21 citations into it; #1,164 of 2,601 by trust here, #712 by citations
• Reinforcement Learning — 20 citations into it
• Aerospace Engineering — 14 citations into it
• Model Architectures — 14 citations into it

Whom Wenyuan Zeng trusts overall

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

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

Who trusts Wenyuan Zeng overall

1. Chaoqi Wang
2. Sidak Pal Singh
3. Xu Han
4. Dan Alistarh
5. Elias Frantar
6. Han-Jia Ye
7. Roger Baker Grosse
8. Guodong Zhang
9. Sanja Fidler
10. Pranav Singh Chib

Papers from Wenyuan Zeng's perspective

Ordered by how strongly the researchers Wenyuan Zeng 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. ImageNet classification with deep convolutional neural networks
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Learning to Reweight Examples for Robust Deep Learning (2018) — 1,690 citations
• End-To-End Interpretable Neural Motion Planner (2019) — 482 citations
• LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World (2020) — 300 citations
• Efficient Summarization with Read-Again and Copy Mechanism (2016) — 101 citations
• V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction (2020) — 654 citations
• Differentiable Compositional Kernel Learning for Gaussian Processes (2018) — 77 citations
• Incorporating Relation Paths in Neural Relation Extraction (2016) — 91 citations
• MLPrune: Multi-Layer Pruning for Automated Neural Network Compression (2018) — 50 citations