Gonzalo Mateos

Artificial Intelligence · 2009–2018

A researcher in the Science corpus: 4 papers; trusts 48 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 28 citations into it; #17,355 of 35,345 by trust here, #20,836 by citations
• Computer Networks and Communications — 24 citations into it; #1,216 of 9,886 by trust here, #2,136 by citations
• Statistical and Nonlinear Physics — 18 citations into it
• Computational Mechanics — 17 citations into it; #1,537 of 10,001 by trust here, #2,203 by citations
• Statistics and Probability — 15 citations into it; #3,251 of 12,932 by trust here, #3,411 by citations
• Computer Vision and Pattern Recognition — 11 citations into it
• Optimization & Training — 10 citations into it
• Molecular Biology — 8 citations into it

Whom Gonzalo Mateos trusts overall

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

1. Robert Tibshirani
2. Hui Zou
3. Trevor J. Hastie
4. Jerome H. Friedman
5. Angelia Nedić
6. Dimitri P. Bertsekas
7. Olaf Sporns
8. Michael G. Rabbat
9. Ioannis D. Schizas
10. Nicolai Meinshausen

Who trusts Gonzalo Mateos overall

1. Ali H. Sayed
2. Wei Shi
3. Dusan Jakovetic
4. Joao Xavier
5. Wotao Yin
6. Angelia Nedić
7. Simone Scardapane
8. Inês Almeida
9. Wei Deng
10. Kun Yuan

Papers from Gonzalo Mateos's perspective

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

1. Regression Shrinkage and Selection via the Lasso
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Atomic Decomposition by Basis Pursuit
7. High-dimensional graphs and variable selection with the Lasso
8. Learning Multiple Layers of Features from Tiny Images
9. Addendum: Regularization and variable selection via the elastic net
10. ImageNet: A large-scale hierarchical image database

Their papers

• Distributed Sparse Linear Regression (2010) — 531 citations
• Connecting the Dots: Identifying Network Structure via Graph Signal Processing (2018) — 401 citations
• Network Topology Inference from Spectral Templates (2016) — 309 citations
• Distributed LMS for Consensus-Based In-Network Adaptive Processing (2009) — 390 citations