Laura Rieger

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 596 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 114 citations into it; #34,552 of 35,345 by trust here, #17,776 by citations
• Safety, Robustness & Interpretability — 83 citations into it
• Computer Vision and Pattern Recognition — 75 citations into it
• Biophysics — 21 citations into it
• Model Architectures — 21 citations into it
• Natural Language Processing — 17 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Materials Chemistry — 6 citations into it

Whom Laura Rieger trusts overall

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

1. Karen Simonyan
2. Matthew D. Zeiler
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Kaiming He
6. Andrew Zisserman
7. Christian Szegedy
8. Jia Deng
9. Andrea Vedaldi
10. Yoshua Bengio

Who trusts Laura Rieger overall

1. Wojciech Samek
2. Chandan Singh
3. Wolfgang Stammer
4. Lin Guan
5. Nimit Sharad Sohoni
6. Lars Kai Hansen
7. Grgoire Montavon
8. Xi Ye
9. Meike Nauta
10. Patrick Schramowski

Papers from Laura Rieger's perspective

Ordered by how strongly the researchers Laura Rieger 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Structuring Neural Networks for More Explainable Predictions (2018) — 15 citations
• Aggregating explanation methods for stable and robust explainability. (2019) — 16 citations
• Aggregating explainability methods for neural networks stabilizes explanations (2019) — 12 citations
• Interpretations are useful: penalizing explanations to align neural networks with prior knowledge (2019) — 258 citations