Maurice Weiler

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 6 papers; trusts 301 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 113 citations into it; #3,974 of 21,575 by trust here, #5,680 by citations
• Model Architectures — 84 citations into it; #354 of 2,278 by trust here, #794 by citations
• Artificial Intelligence — 81 citations into it; #6,316 of 35,345 by trust here, #10,430 by citations
• Computational Mechanics — 20 citations into it; #5,062 of 10,001 by trust here, #3,652 by citations
• Generative Models — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• 3D Vision & Pose — 11 citations into it
• Graph Learning — 10 citations into it

Whom Maurice Weiler trusts overall

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

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

Who trusts Maurice Weiler overall

1. David W. Romero
2. Erik J. Bekkers
3. Taco Cohen
4. Simon Graham
5. Daniel E. Worrall
6. Jiaming Han
7. J. Tomczak
8. Mark Hoogendoorn
9. Marc Finzi
10. Gabriele Cesa

Papers from Maurice Weiler's perspective

Ordered by how strongly the researchers Maurice Weiler 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• Learning Steerable Filters for Rotation Equivariant CNNs (2017) — 448 citations
• Explorations in Homeomorphic Variational Auto-Encoding (2018) — 131 citations
• A General Theory of Equivariant CNNs on Homogeneous Spaces (2018) — 370 citations
• Gauge Equivariant Convolutional Networks and the Icosahedral CNN (2019) — 474 citations
• Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks) (2018) — 49 citations
• General E(2)-Equivariant Steerable CNNs (2019) — 691 citations