Rasmus Rothe

Computer Vision and Pattern Recognition · 2014–2016

A researcher in the Science corpus: 4 papers; trusts 664 researchers and is trusted by 240 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 99 citations into it; #2,506 of 21,575 by trust here, #5,013 by citations
• Artificial Intelligence — 22 citations into it; #25,947 of 35,345 by trust here, #25,809 by citations
• Media Technology — 19 citations into it; #739 of 3,621 by trust here, #1,242 by citations
• Computational Mechanics — 10 citations into it
• Signal Processing — 9 citations into it
• Model Architectures — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Rasmus Rothe trusts overall

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

1. Ross B. Girshick
2. Alex Krizhevsky
3. Kaiming He
4. Geoffrey E. Hinton
5. Karen Simonyan
6. Pedro F. Felzenszwalb
7. Yann LeCun
8. Navneet Dalal
9. Christian Szegedy
10. Ilya Sutskever

Who trusts Rasmus Rothe overall

1. Wojciech Samek
2. Yuval Alaluf
3. Radu Timofte
4. Michele Merler
5. Kimmo Kärkkäinen
6. Steven C. Y. Hung
7. Weixin Liang
8. Jan Hendrik Hosang
9. Or Patashnik
10. Vinay Uday Prabhu

Papers from Rasmus Rothe's perspective

Ordered by how strongly the researchers Rasmus Rothe 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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Non-maximum Suppression for Object Detection by Passing Messages Between Windows (2014) — 211 citations
• Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks (2016) — 938 citations
• Seven Ways to Improve Example-Based Single Image Super Resolution (2015) — 449 citations
• DEX: Deep EXpectation of Apparent Age from a Single Image (2015) — 749 citations