Jana Wäldchen

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 221 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 40 citations into it; #15,608 of 21,575 by trust here, #19,310 by citations
• Plant Science — 22 citations into it; #4,556 of 8,232 by trust here, #7,141 by citations
• Ecology — 17 citations into it
• Artificial Intelligence — 13 citations into it
• Ecological Modeling — 13 citations into it; #971 of 2,530 by trust here, #1,855 by citations
• Molecular Biology — 11 citations into it; #6,996 of 17,773 by trust here, #13,563 by citations
• Model Architectures — 7 citations into it
• Aerospace Engineering — 5 citations into it

Whom Jana Wäldchen trusts overall

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

1. David G. Lowe
2. Maria-Elena Nilsback
3. Andrew Zisserman
4. Yoshua Bengio
5. Kaiming He
6. Alex Krizhevsky
7. Geoffrey E. Hinton
8. Yann LeCun
9. Marco Pautasso
10. Dengsheng Zhang

Who trusts Jana Wäldchen overall

1. Maximilian Pichler
2. Florian Hartig
3. Patrick Mäder
4. Michael Rzanny
5. Marco Seeland

Papers from Jana Wäldchen's perspective

Ordered by how strongly the researchers Jana Wäldchen 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. ImageNet: A large-scale hierarchical image database
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Machine learning for image based species identification (2018) — 411 citations
• Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review (2017) — 425 citations
• Automated plant species identification—Trends and future directions (2018) — 296 citations