Matheus Gadelha

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 2 papers; trusts 549 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 61 citations into it; #5,049 of 21,575 by trust here, #7,657 by citations
• Computational Mechanics — 35 citations into it; #3,235 of 10,001 by trust here, #4,600 by citations
• 3D Vision & Pose — 17 citations into it; #242 of 689 by trust here, #407 by citations
• Aerospace Engineering — 16 citations into it
• Generative Models — 14 citations into it
• Computer Graphics and Computer-Aided Design — 12 citations into it; #384 of 1,584 by trust here, #566 by citations
• Artificial Intelligence — 10 citations into it
• Geology — 8 citations into it; #233 of 687 by trust here, #423 by citations

Whom Matheus Gadelha trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Karen Simonyan
6. Ross B. Girshick
7. Yann LeCun
8. Yoshua Bengio
9. Andrew Zisserman
10. Hao Su

Who trusts Matheus Gadelha overall

1. Xian-Feng Han
2. Maxim Tatarchenko
3. Yongjun Hong
4. Michael Niemeyer
5. Yongcheng Liu
6. Zhiqin Chen
7. Divya Saxena
8. Eric Chan
9. Yongming Rao
10. Hamid Laga

Papers from Matheus Gadelha's perspective

Ordered by how strongly the researchers Matheus Gadelha 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• 3D Shape Induction from 2D Views of Multiple Objects (2016) — 311 citations
• Multiresolution Tree Networks for 3D Point Cloud Processing (2018) — 271 citations