Zeyi Wen

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 92 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 185 citations into it; #25,908 of 35,345 by trust here, #16,471 by citations
• Federated & Distributed Learning — 76 citations into it
• Privacy & Security — 49 citations into it
• Optimization & Training — 28 citations into it
• Computer Networks and Communications — 23 citations into it
• Electrical and Electronic Engineering — 23 citations into it
• Safety, Robustness & Interpretability — 21 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it

Whom Zeyi Wen trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Yann LeCun
5. Diederik P. Kingma
6. Kaiming He
7. Ilya Sutskever
8. Wenliang Du
9. H. Brendan McMahan
10. Yang Liu

Who trusts Zeyi Wen overall

1. Qinbin Li
2. Haftay Gebreslasie Abreha
3. Bingsheng He

Papers from Zeyi Wen's perspective

Ordered by how strongly the researchers Zeyi Wen 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Practical Federated Gradient Boosting Decision Trees (2019) — 220 citations
• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019) — 1,461 citations