Maximilian Alber

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 6 papers; trusts 222 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 108 citations into it; #9,719 of 35,345 by trust here, #4,420 by citations
• Computer Vision and Pattern Recognition — 78 citations into it; #12,549 of 21,575 by trust here, #5,161 by citations
• Safety, Robustness & Interpretability — 65 citations into it; #848 of 2,251 by trust here, #405 by citations
• Biophysics — 24 citations into it
• Model Architectures — 20 citations into it
• Optimization & Training — 15 citations into it
• Natural Language Processing — 14 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Maximilian Alber trusts overall

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

1. Karen Simonyan
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yoshua Bengio
6. Matthew D. Zeiler
7. Barret Zoph
8. Yann LeCun
9. Andrew Zisserman
10. Kaiming He

Who trusts Maximilian Alber overall

1. Wojciech Samek
2. Christopher J. Anders
3. Grgoire Montavon
4. K. Müller
5. Giulia Vilone
6. Julius Adebayo
7. Amitojdeep Singh
8. Piyawat Lertvittayakumjorn
9. Sara Hooker
10. Ann-Kathrin Dombrowski

Papers from Maximilian Alber's perspective

Ordered by how strongly the researchers Maximilian Alber 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• The (Un)reliability of saliency methods (2017) — 806 citations
• Learning how to explain neural networks: PatternNet and PatternAttribution (2017) — 371 citations
• Explanations can be manipulated and geometry is to blame (2019) — 401 citations
• Backprop Evolution (2018) — 9 citations
• iNNvestigate neural networks! (2018) — 380 citations
• PatternNet and PatternLRP - Improving the interpretability of neural networks (2017) — 65 citations