Keith Rush

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 19 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it; #18,481 of 35,345 by trust here, #8,077 by citations
• Optimization & Training — 46 citations into it; #2,465 of 2,601 by trust here, #1,134 by citations
• Federated & Distributed Learning — 19 citations into it; #156 of 319 by trust here, #93 by citations
• Computer Vision and Pattern Recognition — 13 citations into it
• Privacy & Security — 12 citations into it
• Management Science and Operations Research — 10 citations into it
• Efficiency & Compression — 6 citations into it

Whom Keith Rush trusts overall

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

1. H. Brendan McMahan
2. Alex Krizhevsky
3. Yann LeCun
4. Geoffrey E. Hinton
5. Jakub Konecný
6. Yoshua Bengio
7. Diederik P. Kingma
8. Sebastian Caldas
9. Kaiming He
10. John C. Duchi

Who trusts Keith Rush overall

1. Qinbin Li
2. Alireza Fallah
3. Othmane Marfoq

Papers from Keith Rush's perspective

Ordered by how strongly the researchers Keith Rush trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Learning Multiple Layers of Features from Tiny Images
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Improving Federated Learning Personalization via Model Agnostic Meta Learning (2019) — 688 citations
• Adaptive Federated Optimization (2020) — 2,039 citations
• Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning (2020) — 14 citations