Daniel J. Beutel

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it
• Federated & Distributed Learning — 24 citations into it
• Optimization & Training — 12 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Signal Processing — 8 citations into it

Whom Daniel J. Beutel trusts overall

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

1. Tian Li
2. H. Brendan McMahan
3. Martín Abadi
4. Timo Sztyler
5. Alex Krizhevsky
6. Keith Bonawitz
7. Kaiming He
8. Geoffrey E. Hinton
9. Jianmo Ni
10. Olga Russakovsky

Who trusts Daniel J. Beutel overall

1. Samuel Horváth
2. Xinchi Qiu

Papers from Daniel J. Beutel's perspective

Ordered by how strongly the researchers Daniel J. Beutel 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. ImageNet Large Scale Visual Recognition Challenge
8. Learning Multiple Layers of Features from Tiny Images
9. Attention is All you Need (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• Flower: A Friendly Federated Learning Research Framework (2020) — 1,331 citations
• A first look into the carbon footprint of federated learning (2020) — 122 citations