Sean Augenstein

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #23,030 of 35,345 by trust here, #6,422 by citations
• Privacy & Security — 13 citations into it
• Safety, Robustness & Interpretability — 10 citations into it
• Natural Language Processing — 9 citations into it
• Optimization & Training — 9 citations into it; #1,940 of 2,601 by trust here, #680 by citations
• Sociology and Political Science — 6 citations into it

Whom Sean Augenstein trusts overall

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

1. Cynthia Dwork
2. H. Brendan McMahan
3. Ilya Mironov
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Alex Krizhevsky
7. Yoshua Bengio
8. J. Urgen Schmidhuber
9. Yann LeCun
10. Aleksei Triastcyn

Who trusts Sean Augenstein overall

1. Shahab Asoodeh
2. Borja Balle
3. Peter Kairouz
4. Zachary B. Charles

Papers from Sean Augenstein's perspective

Ordered by how strongly the researchers Sean Augenstein 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Learning Multiple Layers of Features from Tiny Images
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Federated Learning for Mobile Keyboard Prediction (2018) — 1,848 citations
• Generative Models for Effective ML on Private, Decentralized Datasets (2019) — 211 citations