Rafael Blanquero

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 5 papers; trusts 33 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it; #12,298 of 35,345 by trust here, #20,377 by citations
• Statistics and Probability — 40 citations into it
• Computer Vision and Pattern Recognition — 36 citations into it
• Statistical Learning Theory — 23 citations into it; #717 of 1,038 by trust here, #893 by citations
• Algorithms & Complexity — 18 citations into it; #309 of 516 by trust here, #365 by citations
• Information Systems — 18 citations into it
• Optimization & Training — 17 citations into it
• Safety, Robustness & Interpretability — 16 citations into it

Whom Rafael Blanquero trusts overall

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

1. Eric R. Ziegel
2. Emilio Carrizosa
3. Frédéric Ferraty
4. Trevor J. Hastie
5. William E. Hart
6. Leo Breiman
7. Sicco Verwer
8. Jack Dunn
9. Philippe Vieu
10. Dimitris J. Bertsimas

Who trusts Rafael Blanquero overall

1. Emilio Carrizosa
2. Vinícius G. Costa
3. Sicco Verwer
4. Cristina Molero-Río
5. Dolores Romero Morales
6. Carlos Eduardo Pedreira
7. Yingqian Zhang
8. Jimmy J. Lin
9. Emir Demirovi'c
10. Claudio Gambella

Papers from Rafael Blanquero's perspective

Ordered by how strongly the researchers Rafael Blanquero 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Regression Shrinkage and Selection via the Lasso
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• On sparse optimal regression trees (2021) — 13 citations
• Optimal randomized classification trees (2021) — 52 citations
• Sparsity in Optimal Randomized Classification Trees (2020) — 51 citations
• Selection of time instants and intervals with Support Vector Regression for multivariate functional data (2020) — 12 citations
• Functional-bandwidth kernel for Support Vector Machine with Functional Data: An alternating optimization algorithm (2019) — 20 citations