Klaus‐Robert Müller

Artificial Intelligence · 2011–2022

A researcher in the Science corpus: 16 papers; trusts 571 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

Artificial Intelligence — 490 citations into it; #9,956 of 35,345 by trust here, #5,788 by citations
Materials Chemistry — 351 citations into it; #614 of 11,513 by trust here, #1,147 by citations
Computer Vision and Pattern Recognition — 247 citations into it; #11,232 of 21,575 by trust here, #8,612 by citations
Computational Theory and Mathematics — 219 citations into it; #1,621 of 12,182 by trust here, #151 by citations
Molecular Biology — 185 citations into it
Atomic and Molecular Physics, and Optics — 158 citations into it; #636 of 7,135 by trust here, #313 by citations
Computational Chemistry & Drug Discovery — 145 citations into it; #51 of 425 by trust here, #31 by citations
Safety, Robustness & Interpretability — 139 citations into it; #704 of 2,251 by trust here, #748 by citations

Whom Klaus‐Robert Müller trusts overall

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

Who trusts Klaus‐Robert Müller overall

Papers from Klaus‐Robert Müller 's perspective

Ordered by how strongly the researchers Klaus‐Robert Müller trusts endorse each paper. Same corpus, a different order for every researcher.

ImageNet classification with deep convolutional neural networks
Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Gradient-based learning applied to document recognition
Long Short-Term Memory
ImageNet: A large-scale hierarchical image database
Attention is All you Need (Neural Information Processing Systems)
Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
ImageNet Large Scale Visual Recognition Challenge
Learning Multiple Layers of Features from Tiny Images

Their papers

Explaining nonlinear classification decisions with deep Taylor decomposition (2015) — 892 citations
A Unifying Review of Deep and Shallow Anomaly Detection (2020) — 1,067 citations
Higher-Order Explanations of Graph Neural Networks via Relevant Walks (2020) — 310 citations
Fast and accurate modeling of molecular atomization energies with machine learning. (2011) — 1,758 citations
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space (2015) — 732 citations
Machine learning of molecular electronic properties in chemical compound space (2013) — 659 citations
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems (2021) — 675 citations
Inverse design of 3d molecular structures with conditional generative neural networks (2021) — 228 citations
Exploring chemical compound space with quantum-based machine learning (2019) — 330 citations
Visual Interpretation of Kernel‐Based Prediction Models (2011) — 58 citations
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties (2013) — 375 citations
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities (2021) — 127 citations
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations (2022) — 20 citations
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (2019) — 1,312 citations
Layer-Wise Relevance Propagation: An Overview (2019) — 897 citations
Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals (2018) — 152 citations