Fabian B. Fuchs

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 239 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 78 citations into it; #8,172 of 21,575 by trust here, #7,499 by citations
• Artificial Intelligence — 48 citations into it
• Model Architectures — 31 citations into it; #908 of 2,278 by trust here, #1,082 by citations
• Computational Mechanics — 22 citations into it; #3,032 of 10,001 by trust here, #2,361 by citations
• Geology — 16 citations into it
• Aerospace Engineering — 12 citations into it
• Generative Models — 9 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Fabian B. Fuchs trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Kaiming He
5. Yoshua Bengio
6. Ross B. Girshick
7. Ilya Sutskever
8. Max Welling
9. Yann LeCun
10. Christian Szegedy

Who trusts Fabian B. Fuchs overall

1. Petar Velickovic
2. Tess E. Smidt
3. Octavian-Eugen Ganea
4. Mario Geiger
5. Yi Liao
6. David W. Romero
7. Matthew McPartlon
8. Victor Garcia Satorras
9. Zhangyang Gao
10. Shuo Zhang

Papers from Fabian B. Fuchs's perspective

Ordered by how strongly the researchers Fabian B. Fuchs 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking (2018) — 119 citations
• SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks (2020) — 953 citations
• On the Limitations of Representing Functions on Sets (2019) — 186 citations
• End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning (2019) — 16 citations