Andreas Eitel

Computer Vision and Pattern Recognition · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 180 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 45 citations into it; #13,432 of 21,575 by trust here, #9,872 by citations
• Artificial Intelligence — 26 citations into it; #21,941 of 35,345 by trust here, #15,004 by citations
• Control and Systems Engineering — 22 citations into it
• Aerospace Engineering — 17 citations into it; #3,392 of 6,421 by trust here, #2,193 by citations
• Reinforcement Learning — 9 citations into it
• Robotics & Control — 8 citations into it
• Biomedical Engineering — 5 citations into it

Whom Andreas Eitel trusts overall

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

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

Who trusts Andreas Eitel overall

1. Oliver Lemon
2. Lei Tai
3. Jamie Sherrah
4. He Zhang
5. Jose L. Part
6. Nicolas Audebert
7. Nuno C. Garcia
8. Kuan Fang
9. Luca Caltagirone
10. Giulia Pasquale

Papers from Andreas Eitel's perspective

Ordered by how strongly the researchers Andreas Eitel 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. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Multimodal deep learning for robust RGB-D object recognition (2015) — 684 citations
• Learning to Singulate Objects using a Push Proposal Network (2017) — 86 citations
• Optimization Beyond the Convolution: Generalizing Spatial Relations with End-to-End Metric Learning (2017) — 19 citations