Guowen Xu

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 22 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it; #23,566 of 35,345 by trust here, #11,192 by citations
• Privacy & Security — 32 citations into it
• Safety, Robustness & Interpretability — 20 citations into it
• Optimization & Training — 8 citations into it
• Health Informatics — 5 citations into it

Whom Guowen Xu trusts overall

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

1. L. Eon Bottou
2. Reza Shokri
3. Dan Boneh
4. Johannes Blömer
5. Justin Thaler
6. Florian Tramèr
7. Mihir Bellare
8. Zvika Brakerski
9. Whitfield Diffie
10. Matthew D. Zeiler

Who trusts Guowen Xu overall

1. Xuefei Yin
2. Li Li
3. Yanming Zhu
4. Jiankun Hu

Papers from Guowen Xu's perspective

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

Their papers

• Privacy-Enhanced Federated Learning Against Poisoning Adversaries (2021) — 314 citations
• Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence (2020) — 418 citations
• VerifyNet: Secure and Verifiable Federated Learning (2020) — 743 citations
• Towards Efficient and Privacy-Preserving Federated Deep Learning (2019) — 200 citations