Jianyu Chen

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 85 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #28,760 of 35,345 by trust here, #28,481 by citations
• Reinforcement Learning — 41 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it
• Automotive Engineering — 20 citations into it; #2,101 of 3,081 by trust here, #2,499 by citations
• Robotics & Control — 11 citations into it
• Control and Systems Engineering — 10 citations into it; #6,626 of 8,257 by trust here, #5,784 by citations
• Generative Models — 7 citations into it
• Optimization & Training — 7 citations into it

Whom Jianyu Chen trusts overall

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

1. Sergey Levine
2. Diederik P. Kingma
3. Volodymyr Mnih
4. John Schulman
5. David Silver
6. Tuomas Haarnoja
7. Geoffrey E. Hinton
8. Richard S. Sutton
9. Pieter Abbeel
10. Alex Krizhevsky

Who trusts Jianyu Chen overall

1. Pranav Singh Chib
2. Li Chen
3. Pravendra Singh
4. Peng Wu

Papers from Jianyu Chen's perspective

Ordered by how strongly the researchers Jianyu Chen 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Model-free Deep Reinforcement Learning for Urban Autonomous Driving (2019) — 318 citations
• Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning (2020) — 339 citations