Zhuangdi Zhu

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 326 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 171 citations into it; #34,071 of 35,345 by trust here, #23,731 by citations
• Reinforcement Learning — 91 citations into it
• Computer Vision and Pattern Recognition — 50 citations into it
• Federated & Distributed Learning — 30 citations into it
• Control and Systems Engineering — 26 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Computational Theory and Mathematics — 18 citations into it
• Optimization & Training — 16 citations into it

Whom Zhuangdi Zhu 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. Alex Krizhevsky
5. Sergey Levine
6. Yoshua Bengio
7. Kaiming He
8. Yann LeCun
9. Jimmy Ba
10. Ilya Sutskever

Who trusts Zhuangdi Zhu overall

1. Mang Ye
2. Alysa Ziying Tan
3. Lin Zhang
4. Shagun Sodhani
5. Maciej Wolczyk
6. Liang Yu
7. Han Yu
8. Xiuwen Fang
9. Li Shen
10. F. Meier

Papers from Zhuangdi Zhu's perspective

Ordered by how strongly the researchers Zhuangdi Zhu 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. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Transfer Learning in Deep Reinforcement Learning: A Survey (2020) — 899 citations
• Data-Free Knowledge Distillation for Heterogeneous Federated Learning (2021) — 1,012 citations
• Federated Adversarial Debiasing for Fair and Transferable Representations (2021) — 68 citations