Stephan Zheng

Artificial Intelligence · with Yue · 2016–2018

A researcher in the Science corpus: 6 papers; trusts 189 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 101 citations into it; #10,309 of 35,345 by trust here, #7,858 by citations
• Computer Vision and Pattern Recognition — 60 citations into it; #8,348 of 21,575 by trust here, #6,954 by citations
• Reinforcement Learning — 36 citations into it
• Generative Models — 26 citations into it
• Model Architectures — 24 citations into it
• Signal Processing — 22 citations into it
• Probabilistic & Bayesian Methods — 19 citations into it
• Representation & Self-Supervised Learning — 16 citations into it

Whom Stephan Zheng trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Alex Krizhevsky
5. Yoshua Bengio
6. Christian Szegedy
7. Yann LeCun
8. Alex Graves
9. J. Urgen Schmidhuber
10. Kaiming He

Who trusts Stephan Zheng overall

1. Dan Hendrycks
2. Julian Salazar
3. Bai Li
4. Nicholas Carlini
5. Andras Rozsa
6. Samuel F. Dodge
7. Jeremy M. Cohen
8. Hossein Hosseini
9. Beilun Wang
10. Leo Gao

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. 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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Improving the Robustness of Deep Neural Networks via Stability Training (2016) — 678 citations
• Generating Multi-Agent Trajectories using Programmatic Weak Supervision (2018) — 103 citations
• Long-term Forecasting using Tensor-Train RNNs (2017) — 146 citations
• Structured Exploration via Hierarchical Variational Policy Networks (2018) — 6 citations
• Generative Multi-Agent Behavioral Cloning (2018) — 41 citations
• Long-term Forecasting using Higher Order Tensor RNNs (2017) — 95 citations