Mohammad Mohammadi Amiri

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 70 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #5,879 of 35,345 by trust here, #11,072 by citations
• Optimization & Training — 54 citations into it
• Federated & Distributed Learning — 28 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Efficiency & Compression — 27 citations into it
• Computational Mechanics — 25 citations into it; #5,409 of 10,001 by trust here, #8,258 by citations
• Electrical and Electronic Engineering — 19 citations into it; #2,686 of 16,539 by trust here, #9,011 by citations
• Computer Networks and Communications — 17 citations into it; #1,492 of 9,886 by trust here, #3,074 by citations

Whom Mohammad Mohammadi Amiri trusts overall

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

1. Yann LeCun
2. Diederik P. Kingma
3. Alex Krizhevsky
4. David L. Donoho
5. Guangxu Zhu
6. Jimmy Ba
7. Felix Sattler
8. Corinna Cortes
9. Geoffrey E. Hinton
10. H. Brendan McMahan

Who trusts Mohammad Mohammadi Amiri overall

1. Deniz Gündüz
2. Anis Elgabli
3. Khaled Ben Letaief
4. Jihong Park
5. Yuanming Shi
6. Guangxu Zhu
7. Mingzhe Chen
8. Jinhyun Ahn
9. Stefano Savazzi
10. Tomer Sery

Papers from Mohammad Mohammadi Amiri's perspective

Ordered by how strongly the researchers Mohammad Mohammadi Amiri 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Federated Learning Over Wireless Fading Channels (2019) — 626 citations
• Federated Learning With Quantized Global Model Updates (2020) — 160 citations
• Over-the-Air Machine Learning at the Wireless Edge (2019) — 57 citations
• Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air (2019) — 591 citations