Belén Martín-Barragán

Artificial Intelligence · 2008–2021

A researcher in the Science corpus: 6 papers; trusts 58 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #9,404 of 35,345 by trust here, #18,586 by citations
• Computer Vision and Pattern Recognition — 35 citations into it; #8,100 of 21,575 by trust here, #14,478 by citations
• Statistics and Probability — 33 citations into it
• Statistical Learning Theory — 20 citations into it
• Control and Systems Engineering — 12 citations into it; #2,489 of 8,257 by trust here, #4,722 by citations
• Evaluation & Benchmarks — 11 citations into it
• Computational Mechanics — 10 citations into it; #5,237 of 10,001 by trust here, #7,501 by citations
• Molecular Biology — 10 citations into it

Whom Belén Martín-Barragán trusts overall

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

1. Vladimir Naumovich Vapni
2. Trevor J. Hastie
3. Robert Tibshirani
4. Vladimir Naumovich Vapnik
5. Joachim Diederich
6. Emilio Carrizosa
7. Ron Kohavi
8. Yuhai Wu
9. Chih-Jen Lin
10. Peter L. Bartlett

Who trusts Belén Martín-Barragán overall

1. Emilio Carrizosa
2. Dolores Romero Morales
3. Berk Ustun
4. Laura Palagi
5. Cynthia Rudin
6. Amaya Nogales-Gómez
7. Rafael Blanquero
8. Véronique Van Vlasselaer

Papers from Belén Martín-Barragán's perspective

Ordered by how strongly the researchers Belén Martín-Barragán 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19 (2021) — 19 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
• A nested heuristic for parameter tuning in Support Vector Machines (2014) — 32 citations
• Binarized Support Vector Machines (2010) — 45 citations
• Multi-group support vector machines with measurement costs: A biobjective approach (2008) — 20 citations