D. Estrin

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 23 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 82 citations into it; #17,751 of 35,345 by trust here, #6,517 by citations
• Safety, Robustness & Interpretability — 35 citations into it; #1,186 of 2,251 by trust here, #525 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Information Systems — 21 citations into it
• Signal Processing — 20 citations into it
• Optimization & Training — 14 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Recommender Systems — 10 citations into it

Whom D. Estrin trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Cynthia Dwork
5. Yoshua Bengio
6. Yann LeCun
7. Tom Minka
8. X. Zhang
9. Diederik P. Kingma
10. Steffen Rendle

Who trusts D. Estrin overall

1. Li Li
2. Clement Fung

Papers from D. Estrin's perspective

Ordered by how strongly the researchers D. Estrin 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. Learning Multiple Layers of Features from Tiny Images
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Et al
10. Distilling the Knowledge in a Neural Network (arXiv.org)

Their papers

• How To Backdoor Federated Learning (2018) — 2,584 citations
• Collaborative Metric Learning (2017) — 550 citations