Hangyu Zhu

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 259 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 171 citations into it; #23,657 of 35,345 by trust here, #17,110 by citations
• Federated & Distributed Learning — 53 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it
• Optimization & Training — 39 citations into it
• Model Architectures — 24 citations into it
• Computational Theory and Mathematics — 19 citations into it
• Computer Science Applications — 18 citations into it; #976 of 1,855 by trust here, #634 by citations
• Efficiency & Compression — 18 citations into it

Whom Hangyu Zhu trusts overall

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

1. Yann LeCun
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. L. Eon Bottou
5. Sepp Hochreiter
6. Yoshua Bengio
7. Kalyanmoy Deb
8. Kaiming He
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Hangyu Zhu overall

1. Xinle Liang
2. Li Li
3. Yaochu Jin
4. Jinjin Xu
5. Jiawen Kang
6. Yang Liu
7. Haftay Gebreslasie Abreha
8. Shiqing Liu
9. Qian Zhang
10. Yang Chen

Papers from Hangyu Zhu's perspective

Ordered by how strongly the researchers Hangyu Zhu 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
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

• Federated Learning on Non-IID Data: A Survey (2021) — 1,345 citations
• Real-Time Federated Evolutionary Neural Architecture Search (2020) — 89 citations
• Multi-Objective Evolutionary Federated Learning (2018) — 274 citations