Katja Auernhammer

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 38 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it
• Safety, Robustness & Interpretability — 53 citations into it
• Signal Processing — 19 citations into it
• Computer Networks and Communications — 16 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it

Whom Katja Auernhammer trusts overall

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

1. Battista Biggio
2. Nicolas Papernot
3. Ian J. Goodfellow
4. Patrick Drew McDaniel
5. Geoffrey E. Hinton
6. Florian Tramèr
7. Alex Krizhevsky
8. Blaine Nelson
9. Daniel Lowd
10. Chang Liu

Papers from Katja Auernhammer's perspective

Ordered by how strongly the researchers Katja Auernhammer 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. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Intriguing properties of neural networks

Their papers

• Attacks on Machine Learning: Lurking Danger for Accountability (2019) — 7 citations