Avishek Ghosh

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 28 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #16,249 of 35,345 by trust here, #13,544 by citations
• Federated & Distributed Learning — 27 citations into it
• Optimization & Training — 24 citations into it
• Statistics and Probability — 13 citations into it
• Computational Mechanics — 10 citations into it
• Statistical Learning Theory — 10 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Computer Networks and Communications — 7 citations into it; #6,282 of 9,886 by trust here, #4,277 by citations

Whom Avishek Ghosh trusts overall

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

1. Alex Krizhevsky
2. Martin A. Zinkevich
3. Martin J. Wainwright
4. Yann LeCun
5. Geoffrey E. Hinton
6. Dong Yin
7. R. Caruana
8. Yoshua Bengio
9. Mehryar Mohri
10. Stanislav Minsker

Who trusts Avishek Ghosh overall

1. Guodong Long
2. Lie He
3. Yue Tan
4. Qinbin Li
5. Sai Praneeth Karimireddy
6. Hangyu Zhu
7. Christopher Briggs
8. Alysa Ziying Tan
9. Mang Ye
10. Jing Jiang

Papers from Avishek Ghosh's perspective

Ordered by how strongly the researchers Avishek Ghosh trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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

• Robust Federated Learning in a Heterogeneous Environment (2019) — 262 citations
• An Efficient Framework for Clustered Federated Learning (2020) — 1,254 citations