Christopher J. Anders

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 526 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 257 citations into it; #27,103 of 35,345 by trust here, #13,353 by citations
• Computer Vision and Pattern Recognition — 149 citations into it
• Safety, Robustness & Interpretability — 147 citations into it
• Biophysics — 35 citations into it
• Model Architectures — 35 citations into it
• Natural Language Processing — 24 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Molecular Biology — 16 citations into it

Whom Christopher J. Anders trusts overall

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

1. Grgoire Montavon
2. Geoffrey E. Hinton
3. Wojciech Samek
4. Alex Krizhevsky
5. Karen Simonyan
6. Yann LeCun
7. Yoshua Bengio
8. K. Müller
9. Sebastian Lapuschkin
10. Kaiming He

Who trusts Christopher J. Anders overall

1. Sebastian Lapuschkin
2. Wojciech Samek
3. Grgoire Montavon
4. Patrick Schramowski

Papers from Christopher J. Anders's perspective

Ordered by how strongly the researchers Christopher J. Anders 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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

• Explanations can be manipulated and geometry is to blame (2019) — 401 citations
• Understanding Patch-Based Learning by Explaining Predictions (2018) — 6 citations
• Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021) — 1,250 citations
• Fairwashing Explanations with Off-Manifold Detergent (2020) — 107 citations
• Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond (2020) — 87 citations
• Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed (2019) — 20 citations