Yerlan Idelbayev

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 376 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 39 citations into it; #5,250 of 21,575 by trust here, #11,721 by citations
• Artificial Intelligence — 33 citations into it; #7,542 of 35,345 by trust here, #18,186 by citations
• Efficiency & Compression — 28 citations into it; #243 of 1,264 by trust here, #880 by citations
• Optimization & Training — 12 citations into it
• Computational Mechanics — 10 citations into it; #6,831 of 10,001 by trust here, #9,582 by citations
• Model Architectures — 10 citations into it
• Computational Mathematics — 8 citations into it
• Signal Processing — 8 citations into it

Whom Yerlan Idelbayev trusts overall

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

1. Alex Krizhevsky
2. Yurii E. Nesterov
3. Yann LeCun
4. Geoffrey E. Hinton
5. Kaiming He
6. M. A. Carreira-Perpiñán
7. X. Zhang
8. Yoshua Bengio
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Yerlan Idelbayev overall

1. Mikhail Khodak
2. Isha Garg
3. Alex Renda
4. Dianbo Liu
5. Carl Lemaire
6. Shuxuan Guo
7. Xia Xiao
8. Tailin Liang
9. Neil A. Tenenholtz
10. Igor Fedorov

Papers from Yerlan Idelbayev's perspective

Ordered by how strongly the researchers Yerlan Idelbayev 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• "Learning-Compression" Algorithms for Neural Net Pruning (2018) — 232 citations
• Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer (2020) — 194 citations