Zhouyuan Huo

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 7 papers; trusts 180 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 639 citations into it; #18,875 of 35,345 by trust here, #3,215 by citations
• Optimization & Training — 231 citations into it; #1,826 of 2,601 by trust here, #2,428 by citations
• Privacy & Security — 227 citations into it
• Federated & Distributed Learning — 211 citations into it
• Computer Vision and Pattern Recognition — 108 citations into it; #11,859 of 21,575 by trust here, #14,478 by citations
• Computer Networks and Communications — 81 citations into it
• Computational Mechanics — 46 citations into it
• Electrical and Electronic Engineering — 43 citations into it

Whom Zhouyuan Huo trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Kaiming He
5. Yoshua Bengio
6. L. Eon Bottou
7. Yann LeCun
8. Karen Simonyan
9. Jimmy Ba
10. Ilya Sutskever

Who trusts Zhouyuan Huo overall

1. Eugene Belilovsky
2. Chi-Chung Chen
3. Michael Eickenberg
4. Dinghuai Zhang
5. Hanlin Tang
6. Qiang Yang
7. Yu-Wei Kao
8. Edouard Oyallon
9. Chiheon Kim
10. Chia-Lin Yang

Papers from Zhouyuan Huo's perspective

Ordered by how strongly the researchers Zhouyuan Huo 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Advances and Open Problems in Federated Learning (2019) — 9,019 citations
• Decoupled Parallel Backpropagation with Convergence Guarantee (2018) — 107 citations
• Training Neural Networks Using Features Replay (2018) — 78 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• On Large-Cohort Training for Federated Learning (2021) — 126 citations
• A Field Guide to Federated Optimization (2021) — 487 citations
• Faster On-Device Training Using New Federated Momentum Algorithm (2020) — 58 citations