Andrew P. Aitken

Computer Vision and Pattern Recognition · 2016

A researcher in the Science corpus: 4 papers; trusts 387 researchers and is trusted by 533 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 152 citations into it; #1,857 of 21,575 by trust here, #728 by citations
• Media Technology — 65 citations into it; #223 of 3,621 by trust here, #16 by citations
• Artificial Intelligence — 29 citations into it
• Generative Models — 22 citations into it; #611 of 1,956 by trust here, #116 by citations
• Computational Mechanics — 14 citations into it
• Model Architectures — 13 citations into it
• Optimization & Training — 8 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Andrew P. Aitken trusts overall

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

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

Who trusts Andrew P. Aitken overall

1. Yulun Zhang
2. Kai Zhang
3. Radu Timofte
4. Xintao Wang
5. Eirikur Agustsson
6. Simon Niklaus
7. Saeed Anwar
8. Jingyun Liang
9. Yapeng Tian
10. Yochai Blau

Papers from Andrew P. Aitken's perspective

Ordered by how strongly the researchers Andrew P. Aitken 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. ImageNet: A large-scale hierarchical image database
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (2016) — 12,248 citations
• Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (2016) — 6,030 citations
• Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation (2016) — 740 citations
• Is the deconvolution layer the same as a convolutional layer? (2016) — 148 citations