Eider Moore

Artificial Intelligence · 2016

A researcher in the Science corpus: 2 papers; trusts 28 researchers and is trusted by 158 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #1,974 of 35,345 by trust here, #1,354 by citations
• Optimization & Training — 39 citations into it; #1,126 of 2,601 by trust here, #836 by citations
• Privacy & Security — 18 citations into it
• Computational Mechanics — 14 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it
• Management Science and Operations Research — 11 citations into it
• Model Architectures — 7 citations into it

Whom Eider Moore trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. John C. Duchi
6. Sergey Ioffe
7. Diederik P. Kingma
8. Ian J. Goodfellow
9. Yuchen Zhang
10. Christian Szegedy

Who trusts Eider Moore overall

1. Jakub Konecný
2. Yang Liu
3. Jianyu Wang
4. H. Brendan McMahan
5. Chaoyang He
6. Tian Li
7. Cong Xie
8. Neel Guha
9. Jihong Park
10. Lichao Sun

Papers from Eider Moore's perspective

Ordered by how strongly the researchers Eider Moore 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. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Communication-Efficient Learning of Deep Networks from Decentralized Data (2016) — 25,704 citations
• Federated Learning of Deep Networks using Model Averaging (2016) — 1,281 citations