Edilson de Aguiar

Computer Vision and Pattern Recognition · 2009–2017

A researcher in the Science corpus: 3 papers; trusts 40 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 54 citations into it; #16,899 of 21,575 by trust here, #6,954 by citations
• Control and Systems Engineering — 6 citations into it; #7,985 of 8,257 by trust here, #1,213 by citations
• Computational Mechanics — 5 citations into it

Whom Edilson de Aguiar trusts overall

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

1. L. Eon Bottou
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. David G. Lowe
5. Yann LeCun
6. Alex Krizhevsky
7. Frank Tong
8. Pedro F. Felzenszwalb
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Edilson de Aguiar overall

1. Hanbyul Joo
2. Fei Han
3. Zerong Zheng
4. Tomas Simon

Papers from Edilson de Aguiar's perspective

Ordered by how strongly the researchers Edilson de Aguiar 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

• Efficient ConvNet-based marker-less motion capture in general scenes with a low number of cameras (2015) — 162 citations
• Motion capture using joint skeleton tracking and surface estimation (2009) — 464 citations
• Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order (2017) — 730 citations