Maziar Sanjabi

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 9 papers; trusts 255 researchers and is trusted by 141 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 171 citations into it; #2,122 of 35,345 by trust here, #1,982 by citations
• Optimization & Training — 97 citations into it; #930 of 2,601 by trust here, #373 by citations
• Computational Mechanics — 64 citations into it
• Computer Vision and Pattern Recognition — 58 citations into it
• Federated & Distributed Learning — 39 citations into it; #9 of 319 by trust here, #16 by citations
• Computer Networks and Communications — 37 citations into it; #897 of 9,886 by trust here, #377 by citations
• Computational Theory and Mathematics — 30 citations into it
• Electrical and Electronic Engineering — 24 citations into it; #4,199 of 16,539 by trust here, #2,992 by citations

Whom Maziar Sanjabi trusts overall

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

1. Yurii E. Nesterov
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yann LeCun
5. Yoshua Bengio
6. Francis R. Bach
7. Diederik P. Kingma
8. Kaiming He
9. Michael I. Jordan
10. Ilya Sutskever

Who trusts Maziar Sanjabi overall

1. Tian Li
2. Jianyu Wang
3. Zachary B. Charles
4. Virginia Smith
5. Yae Jee Cho
6. Qinbin Li
7. Alireza Fallah
8. Aryan Mokhtari
9. Anit Kumar Sahu
10. Young Geun Kim

Papers from Maziar Sanjabi's perspective

Ordered by how strongly the researchers Maziar Sanjabi 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. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
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 Multi-Task Learning (2017) — 2,150 citations
• Federated Optimization in Heterogeneous Networks (2018) — 8,339 citations
• Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods (2019) — 397 citations
• Fair Resource Allocation in Federated Learning (2019) — 1,024 citations
• Tilted Empirical Risk Minimization (2020) — 155 citations
• Optimal Joint Base Station Assignment and Beamforming for Heterogeneous Networks (2014) — 70 citations
• FedDANE: A Federated Newton-Type Method (2019) — 175 citations
• On the Convergence of Federated Optimization in Heterogeneous Networks (2018) — 375 citations
• On the Linear Convergence of the Approximate Proximal Splitting Method for Non-smooth Convex Optimization (2014) — 18 citations