Johannes Klicpera

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 8 papers; trusts 167 researchers and is trusted by 99 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 127 citations into it; #4,651 of 35,345 by trust here, #8,999 by citations
• Graph Learning — 108 citations into it; #125 of 693 by trust here, #179 by citations
• Materials Chemistry — 54 citations into it; #338 of 11,513 by trust here, #4,660 by citations
• Computer Vision and Pattern Recognition — 50 citations into it
• Statistical and Nonlinear Physics — 48 citations into it; #1,236 of 10,127 by trust here, #1,867 by citations
• Computational Theory and Mathematics — 44 citations into it; #600 of 12,182 by trust here, #912 by citations
• Model Architectures — 39 citations into it
• Molecular Biology — 27 citations into it; #2,153 of 17,773 by trust here, #7,124 by citations

Whom Johannes Klicpera trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Max Welling
5. Kaiming He
6. Alex Krizhevsky
7. Yann LeCun
8. Ilya Sutskever
9. Risi Kondor
10. Taco Cohen

Who trusts Johannes Klicpera overall

1. Yi Liao
2. Tess E. Smidt
3. Yuanqi Du
4. Daniel Zügner
5. Yidong Liao
6. Stephan Günnemann
7. Xiyuan Wang
8. Ilyes Batatia
9. Kaveh Hassani
10. Hao Zhu

Papers from Johannes Klicpera's perspective

Ordered by how strongly the researchers Johannes Klicpera 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
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

• Directional Message Passing for Molecular Graphs (2020) — 1,129 citations
• Predict then Propagate: Graph Neural Networks meet Personalized PageRank (2018) — 2,108 citations
• Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules (2020) — 430 citations
• GemNet: Universal Directional Graph Neural Networks for Molecules (2021) — 633 citations
• Scaling Graph Neural Networks with Approximate PageRank (2020) — 434 citations
• Diffusion Improves Graph Learning (2019) — 890 citations
• Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank (2018) — 61 citations
• Predict then Propagate: Combining neural networks with personalized pagerank for classification on graphs (2018) — 171 citations