Stephan Zheng

Artificial Intelligence · with Xiong · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 104 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 179 citations into it
• Natural Language Processing — 78 citations into it
• Computer Vision and Pattern Recognition — 67 citations into it
• Reinforcement Learning — 63 citations into it
• Control and Systems Engineering — 12 citations into it
• Generative Models — 12 citations into it
• Computational Theory and Mathematics — 8 citations into it
• Optimization & Training — 8 citations into it

Whom Stephan Zheng trusts overall

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

1. Geoffrey E. Hinton
2. Richard S. Sutton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Alex Krizhevsky
6. Ilya Sutskever
7. Yann LeCun
8. Sergey Levine
9. Jimmy Ba
10. Alex Graves

Papers from Stephan Zheng's perspective

Ordered by how strongly the researchers Stephan Zheng 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. ImageNet Large Scale Visual Recognition Challenge
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Robustness Gym: Unifying the NLP Evaluation Landscape (2021) — 167 citations
• Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards (2019) — 142 citations
• ESPRIT: Explaining Solutions to Physical Reasoning Tasks (2020) — 29 citations
• Learning World Graphs to Accelerate Hierarchical Reinforcement Learning (2019) — 23 citations