Patrick Mäder
Computer Vision and Pattern Recognition · 2012–2018
A researcher in the Science corpus: 6 papers; trusts 222 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 42 citations into it; #15,605 of 21,575 by trust here, #19,310 by citations
- Plant Science — 22 citations into it; #4,555 of 8,232 by trust here, #7,141 by citations
- Artificial Intelligence — 18 citations into it
- Ecology — 17 citations into it
- Ecological Modeling — 13 citations into it; #970 of 2,530 by trust here, #1,855 by citations
- Molecular Biology — 11 citations into it; #6,995 of 17,773 by trust here, #13,563 by citations
- Model Architectures — 7 citations into it
- Information Systems — 6 citations into it; #8,716 of 8,771 by trust here, #6,770 by citations
Whom Patrick Mäder trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David G. Lowe
- Maria-Elena Nilsback
- Andrew Zisserman
- Yoshua Bengio
- Kaiming He
- Alex Krizhevsky
- Marco Pautasso
- Yann LeCun
- Geoffrey E. Hinton
- Dengsheng Zhang
Who trusts Patrick Mäder overall
Papers from Patrick Mäder's perspective
Ordered by how strongly the researchers Patrick Mäder trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Going deeper with convolutions
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
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
- Software traceability: trends and future directions (2014) — 358 citations
- Mind the gap: assessing the conformance of software traceability to relevant guidelines (2014) — 75 citations
- Traceability Fundamentals (2012) — 127 citations