Franziska Horn

Artificial Intelligence · 2014–2016

A researcher in the Science corpus: 3 papers; trusts 10 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #8,467 of 35,345 by trust here, #9,870 by citations
• Natural Language Processing — 18 citations into it; #1,838 of 5,688 by trust here, #2,172 by citations
• Safety, Robustness & Interpretability — 16 citations into it; #925 of 2,251 by trust here, #1,481 by citations
• Computer Vision and Pattern Recognition — 14 citations into it
• Biophysics — 8 citations into it

Whom Franziska Horn trusts overall

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

1. Yoon Kim
2. Grgoire Montavon
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Matthew D. Zeiler
6. Wojciech Samek
7. K. Müller
8. Tomas Mikolov
9. Yann LeCun
10. Sebastian Bach

Who trusts Franziska Horn overall

1. Wojciech Samek
2. Grgoire Montavon
3. Piyawat Lertvittayakumjorn
4. Giulia Vilone
5. K. Müller
6. Yonatan Belinkov
7. Leila Arras
8. Sebastian Lapuschkin
9. Francesca Toni
10. Longo Luca

Papers from Franziska Horn's perspective

Ordered by how strongly the researchers Franziska Horn 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• "What is relevant in a text document?": An interpretable machine learning approach (2016) — 300 citations
• Explaining Predictions of Non-Linear Classifiers in NLP (2016) — 123 citations
• Robust artifactual independent component classification for BCI practitioners (2014) — 313 citations