Othmane Marfoq

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 43 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it
• Federated & Distributed Learning — 46 citations into it
• Optimization & Training — 45 citations into it
• Computer Networks and Communications — 33 citations into it
• ML Systems & Hardware — 17 citations into it
• Electrical and Electronic Engineering — 11 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Algorithms & Complexity — 5 citations into it

Whom Othmane Marfoq trusts overall

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

1. Alex Krizhevsky
2. H. Brendan McMahan
3. Kaiming He
4. Jianyu Wang
5. Geoffrey E. Hinton
6. Tian Li
7. Anit Kumar Sahu
8. Angelia Nedić
9. Yurii E. Nesterov
10. Sébastien Marcel

Who trusts Othmane Marfoq overall

1. Qinbin Li
2. Zeyi Wen

Papers from Othmane Marfoq's perspective

Ordered by how strongly the researchers Othmane Marfoq 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Throughput-Optimal Topology Design for Cross-Silo Federated Learning (2020) — 113 citations
• Federated Multi-Task Learning under a Mixture of Distributions (2021) — 398 citations