Christoph Molnar

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 126 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 148 citations into it; #9,490 of 35,345 by trust here, #12,580 by citations
• Safety, Robustness & Interpretability — 74 citations into it; #716 of 2,251 by trust here, #1,569 by citations
• Computer Vision and Pattern Recognition — 35 citations into it
• Statistics and Probability — 31 citations into it
• Safety Research — 12 citations into it
• AI Ethics, Fairness & Society — 11 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Natural Language Processing — 9 citations into it

Whom Christoph Molnar trusts overall

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

1. Jerome H. Friedman
2. Scott M. Lundberg
3. Giles Hooker
4. Robert Tibshirani
5. Erik Strumbelj
6. Lloyd S. Shapley
7. Geoffrey E. Hinton
8. Mukund Sundararajan
9. Brandon M. Greenwell
10. Marco Tulio Ribeiro

Who trusts Christoph Molnar overall

1. Alicja Gosiewska
2. Waddah Saeed
3. Mateusz Staniak
4. Przemyslaw Biecek
5. Arun Das
6. Giuseppe Casalicchio
7. Jianlong Zhou
8. Susanne Dandl
9. Bernd Bischl
10. Jiayun Dong

Papers from Christoph Molnar's perspective

Ordered by how strongly the researchers Christoph Molnar trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Multi-Objective Counterfactual Explanations (2020) — 322 citations
• Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges (2020) — 546 citations
• Explainable AI Methods - A Brief Overview (2020) — 389 citations
• iml: An R package for Interpretable Machine Learning (2018) — 561 citations
• Visualizing the Feature Importance for Black Box Models (2018) — 219 citations