Roberto Rigamonti

Computer Vision and Pattern Recognition · 2011–2013

A researcher in the Science corpus: 2 papers; trusts 244 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 53 citations into it; #4,500 of 21,575 by trust here, #8,409 by citations
• Artificial Intelligence — 20 citations into it; #19,281 of 35,345 by trust here, #25,040 by citations
• Representation & Self-Supervised Learning — 18 citations into it; #1,967 of 3,145 by trust here, #2,624 by citations
• Computational Mechanics — 15 citations into it; #3,945 of 10,001 by trust here, #2,112 by citations
• Generative Models — 8 citations into it
• Media Technology — 8 citations into it
• Aerospace Engineering — 6 citations into it
• Signal Processing — 6 citations into it

Whom Roberto Rigamonti trusts overall

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

1. David G. Lowe
2. Alex Krizhevsky
3. Marc'Aurelio Ranzato
4. Yann LeCun
5. Geoffrey E. Hinton
6. Y-Lan Boureau
7. Yoshua Bengio
8. Francis R. Bach
9. Julien Mairal
10. Rob Fergus

Who trusts Roberto Rigamonti overall

1. Julien Mairal
2. Anil M. Cheriyadat
3. Georgios Georgiadis
4. Francis R. Bach
5. Max Jaderberg
6. X. Zhang
7. Yangqing Jia
8. Simon Niklaus
9. Junjie Yan
10. Andrea Vedaldi

Papers from Roberto Rigamonti's perspective

Ordered by how strongly the researchers Roberto Rigamonti 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning Separable Filters (2013) — 308 citations
• Are sparse representations really relevant for image classification? (2011) — 221 citations