Sergey Zagoruyko

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 7 papers; trusts 985 researchers and is trusted by 10,791 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 204 citations into it; #160 of 21,575 by trust here, #242 by citations
• Artificial Intelligence — 121 citations into it; #123 of 35,345 by trust here, #435 by citations
• Model Architectures — 53 citations into it; #26 of 2,278 by trust here, #63 by citations
• Representation & Self-Supervised Learning — 29 citations into it
• Optimization & Training — 25 citations into it
• Natural Language Processing — 16 citations into it
• Signal Processing — 14 citations into it
• Aerospace Engineering — 13 citations into it

Whom Sergey Zagoruyko trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Yann LeCun
6. Ross B. Girshick
7. Yoshua Bengio
8. Ilya Sutskever
9. Andrew Zisserman
10. X. Zhang

Who trusts Sergey Zagoruyko overall

1. Quoc V. Le
2. Dan Hendrycks
3. Yoshua Bengio
4. Barret Zoph
5. Liang-Chieh Chen
6. Dacheng Tao
7. Yi Yang
8. Ekin Dogus Cubuk
9. Andrea Vedaldi
10. Alan L. Yuille

Papers from Sergey Zagoruyko's perspective

Ordered by how strongly the researchers Sergey Zagoruyko 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Wide Residual Networks (2016) — 8,993 citations
• End-to-End Object Detection with Transformers (2020) — 19,102 citations
• Learning to compare image patches via convolutional neural networks (2015) — 1,483 citations
• Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer (2016) — 3,142 citations
• A MultiPath Network for Object Detection (2016) — 218 citations
• Scaling the Scattering Transform: Deep Hybrid Networks (2017) — 171 citations
• Scattering Networks for Hybrid Representation Learning (2018) — 99 citations