Maxim Rakhuba

Computer Vision and Pattern Recognition · 2014–2020

A researcher in the Science corpus: 2 papers; trusts 173 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 58 citations into it; #10,407 of 21,575 by trust here, #5,507 by citations
• Artificial Intelligence — 41 citations into it; #18,435 of 35,345 by trust here, #7,728 by citations
• Computational Mathematics — 15 citations into it; #316 of 612 by trust here, #42 by citations
• Model Architectures — 14 citations into it
• Signal Processing — 8 citations into it
• Computational Theory and Mathematics — 6 citations into it
• Media Technology — 5 citations into it

Whom Maxim Rakhuba trusts overall

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

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

Who trusts Maxim Rakhuba overall

1. Huan Wang
2. Shaohui Lin
3. Yongxin Yang
4. José Manuel Álvarez

Papers from Maxim Rakhuba's perspective

Ordered by how strongly the researchers Maxim Rakhuba 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition (2014) — 952 citations
• T-Basis: a Compact Representation for Neural Networks (2020) — 32 citations