Anit Kumar Sahu

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 7 papers; trusts 105 researchers and is trusted by 72 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 201 citations into it; #3,860 of 35,345 by trust here, #2,365 by citations
• Optimization & Training — 116 citations into it; #812 of 2,601 by trust here, #355 by citations
• Federated & Distributed Learning — 80 citations into it; #16 of 319 by trust here, #18 by citations
• Computer Networks and Communications — 71 citations into it; #7,998 of 9,886 by trust here, #3,753 by citations
• Computational Mechanics — 41 citations into it; #8,405 of 10,001 by trust here, #6,877 by citations
• Computer Vision and Pattern Recognition — 38 citations into it
• ML Systems & Hardware — 32 citations into it
• Privacy & Security — 30 citations into it

Whom Anit Kumar Sahu trusts overall

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

1. Virginia Smith
2. Yurii E. Nesterov
3. Yann LeCun
4. H. Brendan McMahan
5. Alex Krizhevsky
6. John C. Duchi
7. Geoffrey E. Hinton
8. Mark W. Schmidt
9. Yoshua Bengio
10. Kaiming He

Who trusts Anit Kumar Sahu overall

1. Tian Li
2. Qinbin Li
3. Zachary B. Charles
4. Jianyu Wang
5. Chaoyang He
6. Othmane Marfoq
7. Sai Praneeth Karimireddy
8. Hong-You Chen
9. Yae Jee Cho
10. Farzin Haddadpour

Papers from Anit Kumar Sahu's perspective

Ordered by how strongly the researchers Anit Kumar Sahu 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. Long Short-Term Memory
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 Optimization in Heterogeneous Networks (2018) — 8,339 citations
• Federated Learning: Challenges, Methods, and Future Directions (2019) — 6,159 citations
• FedDANE: A Federated Newton-Type Method (2019) — 175 citations
• MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling (2019) — 182 citations
• On the Convergence of Federated Optimization in Heterogeneous Networks (2018) — 375 citations
• Convergence Rates for Distributed Stochastic Optimization Over Random Networks (2018) — 76 citations
• Communication-Efficient Distributed Strongly Convex Stochastic Optimization: Non-Asymptotic Rates. (2018) — 15 citations