Lie He

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 139 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 483 citations into it; #34,768 of 35,345 by trust here, #3,304 by citations
• Privacy & Security — 214 citations into it
• Federated & Distributed Learning — 162 citations into it
• Optimization & Training — 136 citations into it
• Safety, Robustness & Interpretability — 94 citations into it
• Computer Networks and Communications — 84 citations into it
• Computer Vision and Pattern Recognition — 62 citations into it
• ML Systems & Hardware — 55 citations into it

Whom Lie He trusts overall

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

1. Dong Yin
2. Yann LeCun
3. El Mahdi El Mhamdi
4. Alex Krizhevsky
5. John C. Duchi
6. Yudong Chen
7. Geoffrey E. Hinton
8. H. Brendan McMahan
9. Kaiming He
10. Yoshua Bengio

Who trusts Lie He overall

1. Ji Liu
2. Sai Praneeth Karimireddy
3. Hanlin Tang
4. Tian Li
5. Martin Jaggi
6. Anastasia Koloskova
7. Virat Shejwalkar
8. Jizhou Huang

Papers from Lie He's perspective

Ordered by how strongly the researchers Lie He 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

• Advances and Open Problems in Federated Learning (2019) — 9,019 citations
• COLA: Decentralized Linear Learning (2018) — 140 citations
• Byzantine-Robust Learning on Heterogeneous Datasets via Resampling (2020) — 42 citations
• Secure Byzantine-Robust Machine Learning (2020) — 68 citations
• Learning from History for Byzantine Robust Optimization (2020) — 260 citations