S. Avestimehr

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 5 papers; trusts 145 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 354 citations into it
• Federated & Distributed Learning — 137 citations into it; #296 of 319 by trust here, #168 by citations
• Optimization & Training — 104 citations into it
• Computer Vision and Pattern Recognition — 80 citations into it
• Efficiency & Compression — 59 citations into it
• Privacy & Security — 43 citations into it
• ML Systems & Hardware — 32 citations into it
• Model Architectures — 18 citations into it

Whom S. Avestimehr trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Chaoyang He
5. Jimmy Ba
6. Yoshua Bengio
7. Diederik P. Kingma
8. Karen Simonyan
9. Yann LeCun
10. X. Zhang

Who trusts S. Avestimehr overall

1. Dacheng Tao

Papers from S. Avestimehr's perspective

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

Their papers

• PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers (2021) — 38 citations
• A Field Guide to Federated Optimization (2021) — 487 citations
• Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search. (2020) — 30 citations
• FedML: A Research Library and Benchmark for Federated Machine Learning (2020) — 716 citations
• Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge (2020) — 580 citations