Gonzalo Martínez-Muñoz

Artificial Intelligence · 2009–2019

A researcher in the Science corpus: 3 papers; trusts 24 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #10,636 of 35,345 by trust here, #19,439 by citations
• Statistics and Probability — 13 citations into it
• Optimization & Training — 12 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it; #9,913 of 21,575 by trust here, #14,822 by citations
• Evaluation & Benchmarks — 9 citations into it
• Statistical Learning Theory — 7 citations into it
• Information Systems — 5 citations into it; #3,659 of 8,771 by trust here, #5,303 by citations

Whom Gonzalo Martínez-Muñoz trusts overall

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

1. Jaka Demsar
2. Thomas G. Dietterich
3. Jerome H. Friedman
4. Leo Breiman
5. Niko Speybroeck
6. Arnold J. Stromberg
7. Pedro M. Domingos
8. Eric Bauer
9. Peter Buhlmann
10. Andreas Buja

Who trusts Gonzalo Martínez-Muñoz overall

1. Md. Nasim Adnan
2. Handing Wang
3. Sebastian Bruch
4. Yongquan Yang
5. Michal Wozniak
6. M. Islam
7. Stefan Lessmann
8. João Mendes-Moreira
9. Yaochu Jin
10. Jimmy J. Lin

Papers from Gonzalo Martínez-Muñoz's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Bagging Predictors
3. Gradient-based learning applied to document recognition
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. The Nature of Statistical Learning Theory
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Scikit-learn: Machine Learning in Python
8. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
9. Regression Shrinkage and Selection via the Lasso
10. Boosting the margin: A new explanation for the effectiveness of voting methods

Their papers

• A comparative analysis of gradient boosting algorithms (2019) — 2,475 citations
• Out-of-bag estimation of the optimal sample size in bagging (2010) — 128 citations
• An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation (2009) — 336 citations