Alireza Fallah

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 131 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 111 citations into it; #10,062 of 35,345 by trust here, #12,580 by citations
• Optimization & Training — 54 citations into it
• Federated & Distributed Learning — 44 citations into it; #145 of 319 by trust here, #215 by citations
• Computational Mechanics — 28 citations into it
• Meta & Continual Learning — 28 citations into it; #515 of 1,041 by trust here, #583 by citations
• Computer Networks and Communications — 27 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Privacy & Security — 10 citations into it

Whom Alireza Fallah trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Chelsea Finn
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Sebastian U. Stich
7. H. Brendan McMahan
8. John C. Duchi
9. Diederik P. Kingma
10. Kaiming He

Who trusts Alireza Fallah overall

1. Zachary B. Charles
2. Aviv Shamsian
3. Jakub Konecný
4. Qinbin Li
5. Zhuangdi Zhu
6. Aryan Mokhtari
7. Jaehoon Oh
8. Alysa Ziying Tan
9. Xuefei Yin
10. Kaiyi Ji

Papers from Alireza Fallah's perspective

Ordered by how strongly the researchers Alireza Fallah 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
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

• Personalized Federated Learning: A Meta-Learning Approach (2020) — 720 citations
• On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms (2019) — 268 citations
• Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach (2020) — 1,273 citations
• Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks (2019) — 33 citations