Pablo Márquez-Neila

Computer Vision and Pattern Recognition · with Fua · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 321 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 93 citations into it; #12,289 of 21,575 by trust here, #10,010 by citations
• Human-Computer Interaction — 28 citations into it
• Artificial Intelligence — 19 citations into it; #28,238 of 35,345 by trust here, #27,551 by citations
• Biomedical Engineering — 11 citations into it; #7,997 of 10,734 by trust here, #5,712 by citations
• Computational Mechanics — 11 citations into it
• Aerospace Engineering — 7 citations into it
• Optimization & Training — 7 citations into it
• Model Architectures — 6 citations into it

Whom Pablo Márquez-Neila trusts overall

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

1. Diederik P. Kingma
2. Matthew D. Zeiler
3. Geoffrey E. Hinton
4. Jimmy Ba
5. Oriol Vinyals
6. Barak A. Pearlmutter
7. Alex Krizhevsky
8. Karen Simonyan
9. Kaiming He
10. Yoshua Bengio

Who trusts Pablo Márquez-Neila overall

1. Stephen Gould
2. Dushyant Mehta
3. Georgios Pavlakos
4. Helge Rhodin
5. Richard I. Hartley
6. Muhammed Kocabas
7. Yucheng Chen
8. Grégory Rogez
9. Andrei Zanfir
10. Xiao Sun

Papers from Pablo Márquez-Neila's perspective

Ordered by how strongly the researchers Pablo Márquez-Neila 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. 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

• Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation (2016) — 260 citations
• Fusing 2D Uncertainty and 3D Cues for Monocular Body Pose Estimation (2016) — 36 citations
• Imposing Hard Constraints on Deep Networks: Promises and Limitations (2017) — 166 citations