Dmitry Kovalev

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 6 papers; trusts 97 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 126 citations into it; #7,165 of 35,345 by trust here, #10,430 by citations
• Optimization & Training — 109 citations into it; #845 of 2,601 by trust here, #1,298 by citations
• Computational Mechanics — 75 citations into it; #4,450 of 10,001 by trust here, #2,447 by citations
• Computer Vision and Pattern Recognition — 32 citations into it; #16,237 of 21,575 by trust here, #18,782 by citations
• Computational Theory and Mathematics — 23 citations into it
• Efficiency & Compression — 23 citations into it
• Federated & Distributed Learning — 21 citations into it; #211 of 319 by trust here, #277 by citations
• Computer Networks and Communications — 19 citations into it

Whom Dmitry Kovalev trusts overall

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

1. Peter Richtárik
2. Shai Shalev-Shwartz
3. Yurii E. Nesterov
4. Aaron Defazio
5. Tong Zhang
6. Martin Takác
7. Alex Krizhevsky
8. Francis R. Bach
9. Diederik P. Kingma
10. Sebastian U. Stich

Who trusts Dmitry Kovalev overall

1. Peter Richtárik
2. Filip Hanzely
3. Ahmed Khaled
4. Nicolas Loizou
5. Eduard A. Gorbunov
6. Othmane Sebbouh
7. Zhize Li
8. Samuel Horváth
9. Constantin Philippenko
10. Konstantin Mishchenko

Papers from Dmitry Kovalev's perspective

Ordered by how strongly the researchers Dmitry Kovalev 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. 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

• Revisiting Stochastic Extragradient (2019) — 83 citations
• Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop (2019) — 180 citations
• From Local SGD to Local Fixed Point Methods for Federated Learning (2020) — 139 citations
• Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization (2020) — 158 citations
• Stochastic Distributed Learning with Gradient Quantization and Variance Reduction (2019) — 143 citations
• Distributed Fixed Point Methods with Compressed Iterates (2019) — 18 citations