Marcel Ackermann

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 33 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it
• Safety, Robustness & Interpretability — 34 citations into it
• Computer Vision and Pattern Recognition — 29 citations into it
• Model Architectures — 6 citations into it

Whom Marcel Ackermann trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Karen Simonyan
5. Grgoire Montavon
6. Wojciech Samek
7. Ilya Sutskever
8. Yoshua Bengio
9. Andrew Zisserman
10. Alexander Binder

Who trusts Marcel Ackermann overall

1. Wojciech Samek
2. Sebastian Lapuschkin
3. Grgoire Montavon
4. Martin Mundt

Papers from Marcel Ackermann's perspective

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

Their papers

• Explanations can be manipulated and geometry is to blame (2019) — 401 citations
• Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals (2018) — 152 citations