Kumar Kshitij Patel

Optimization & Training · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 138 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 125 citations into it; #1,147 of 2,601 by trust here, #1,661 by citations
• Artificial Intelligence — 123 citations into it; #9,987 of 35,345 by trust here, #17,110 by citations
• Computational Mechanics — 63 citations into it
• Management Science and Operations Research — 26 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it
• Statistics and Probability — 23 citations into it
• Federated & Distributed Learning — 20 citations into it
• Computer Networks and Communications — 19 citations into it

Whom Kumar Kshitij Patel trusts overall

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

1. Francis R. Bach
2. Ohad Shamir
3. Tong Zhang
4. Yurii E. Nesterov
5. David Ruppert
6. John C. Duchi
7. Geoffrey E. Hinton
8. Boris Polyak
9. Martin A. Zinkevich
10. Alex Krizhevsky

Who trusts Kumar Kshitij Patel overall

1. Sai Praneeth Karimireddy
2. Sebastian U. Stich
3. Constantin Philippenko
4. Satyen Kale
5. Aymeric Dieuleveut
6. Maruan Al-Shedivat
7. Mehryar Mohri
8. Ahmed Khaled
9. Anastasia Koloskova
10. Sashank J. Reddi

Papers from Kumar Kshitij Patel's perspective

Ordered by how strongly the researchers Kumar Kshitij Patel 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. ImageNet: A large-scale hierarchical image database
7. Learning Multiple Layers of Features from Tiny Images
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

• Communication trade-offs for synchronized distributed SGD with large step size (2019) — 27 citations
• Is Local SGD Better than Minibatch SGD? (2020) — 290 citations
• Minibatch vs Local SGD for Heterogeneous Distributed Learning (2020) — 228 citations
• Communication trade-offs for Local-SGD with large step size (2019) — 44 citations