Sven Dähne
Artificial Intelligence · 2014–2018
A researcher in the Science corpus: 6 papers; trusts 148 researchers and is trusted by 27 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 75 citations into it; #7,209 of 35,345 by trust here, #3,757 by citations
- Computer Vision and Pattern Recognition — 60 citations into it; #12,547 of 21,575 by trust here, #5,205 by citations
- Safety, Robustness & Interpretability — 42 citations into it; #578 of 2,251 by trust here, #312 by citations
- Biophysics — 21 citations into it
- Model Architectures — 14 citations into it
- Natural Language Processing — 10 citations into it
- Optimization & Training — 6 citations into it
Whom Sven Dähne trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Karen Simonyan
- Diederik P. Kingma
- Geoffrey E. Hinton
- Matthew D. Zeiler
- Alex Krizhevsky
- Andrew Zisserman
- Yoshua Bengio
- Yann LeCun
- Ilya Sutskever
- Rob Fergus
Who trusts Sven Dähne overall
Papers from Sven Dähne's perspective
Ordered by how strongly the researchers Sven Dähne 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Gradient-based learning applied to document recognition
- 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)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- The (Un)reliability of saliency methods (2017) — 806 citations
- Learning how to explain neural networks: PatternNet and PatternAttribution (2017) — 371 citations
- On the interpretation of weight vectors of linear models in multivariate neuroimaging (2014) — 1,365 citations
- Investigating the influence of noise and distractors on the interpretation of neural networks (2016) — 131 citations
- iNNvestigate neural networks! (2018) — 380 citations
- PatternNet and PatternLRP - Improving the interpretability of neural networks (2017) — 65 citations