Matthias Fey

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 7 papers; trusts 346 researchers and is trusted by 165 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 169 citations into it; #1,924 of 35,345 by trust here, #4,205 by citations
• Graph Learning — 138 citations into it; #28 of 693 by trust here, #83 by citations
• Computer Vision and Pattern Recognition — 82 citations into it; #2,898 of 21,575 by trust here, #3,613 by citations
• Statistical and Nonlinear Physics — 55 citations into it
• Molecular Biology — 48 citations into it
• Model Architectures — 40 citations into it; #1,891 of 2,278 by trust here, #969 by citations
• Representation & Self-Supervised Learning — 40 citations into it
• Materials Chemistry — 37 citations into it

Whom Matthias Fey trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Yann LeCun
5. Alex Krizhevsky
6. Thomas Kipf
7. Ilya Sutskever
8. Max Welling
9. Sepp Hochreiter
10. Kaiming He

Who trusts Matthias Fey overall

1. Guohao Li
2. Weihua Hu
3. Vijay Prakash Dwivedi
4. Uri Alon
5. Christopher Morris
6. Cristian Bodnar
7. Ziniu Hu
8. Yanqiao Zhu
9. Ladislav Rampášek
10. Seyed Mehran Kazemi

Papers from Matthias Fey's perspective

Ordered by how strongly the researchers Matthias Fey 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Fast Graph Representation Learning with PyTorch Geometric (2019) — 5,499 citations
• SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels (2017) — 511 citations
• Group Equivariant Capsule Networks (2018) — 119 citations
• Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks (2019) — 51 citations
• OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs (2021) — 508 citations
• Open Graph Benchmark: Datasets for Machine Learning on Graphs (2020) — 3,715 citations
• Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (2018) — 2,039 citations