Simon Donné

Computer Vision and Pattern Recognition · 2018

A researcher in the Science corpus: 1 paper; trusts 236 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 37 citations into it; #8,088 of 21,575 by trust here, #12,172 by citations
• Computational Mechanics — 25 citations into it; #2,977 of 10,001 by trust here, #3,908 by citations
• Aerospace Engineering — 19 citations into it
• 3D Vision & Pose — 18 citations into it; #268 of 689 by trust here, #485 by citations
• Computer Graphics and Computer-Aided Design — 12 citations into it; #480 of 1,584 by trust here, #726 by citations
• Geology — 9 citations into it; #212 of 687 by trust here, #360 by citations
• Model Architectures — 6 citations into it

Whom Simon Donné trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. Sergey Ioffe
6. Ali O. Ulusoy
7. Christian Häne
8. Charles Ruizhongtai Qi
9. Yann LeCun
10. Yoshua Bengio

Who trusts Simon Donné overall

1. Julian Chibane
2. Zhiqin Chen
3. Lars M. Mescheder
4. Rundi Wu
5. Michael Niemeyer
6. Michael Oechsle
7. Rohan Chabra

Papers from Simon Donné's perspective

Ordered by how strongly the researchers Simon Donné 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Marching Cubes: Learning Explicit Surface Representations (2018) — 297 citations