Paul Guerrero

Computational Mechanics · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 38 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 44 citations into it; #4,472 of 10,001 by trust here, #4,450 by citations
• Computer Vision and Pattern Recognition — 33 citations into it
• Computer Graphics and Computer-Aided Design — 24 citations into it; #882 of 1,584 by trust here, #843 by citations
• Geology — 10 citations into it
• Aerospace Engineering — 7 citations into it
• Model Architectures — 7 citations into it

Whom Paul Guerrero trusts overall

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

1. Jonathan Masci
2. Szymon Rusinkiewicz
3. Davide Boscaini
4. Nina Amenta
5. Gaël Guennebaud
6. Markus H. Gross
7. Carsten E. M. C. Lange
8. Alexandre Boulch
9. Michael M. Bronstein
10. Charles Ruizhongtai Qi

Who trusts Paul Guerrero overall

1. Yifan Wang
2. Kang Yin
3. Matthias Fey
4. Adrien Poulenard
5. Xu Yan
6. Charles Ruizhongtai Qi
7. Marie-Julie Rakotosaona
8. M. Ovsjanikov
9. Songyou Peng
10. Sebastian Koch

Papers from Paul Guerrero's perspective

Ordered by how strongly the researchers Paul Guerrero trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds (2019) — 406 citations
• PCPNet Learning Local Shape Properties from Raw Point Clouds (2017) — 426 citations