Alberto Suárez

Artificial Intelligence · 1999–2014

A researcher in the Science corpus: 5 papers; trusts 35 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #11,701 of 35,345 by trust here, #19,018 by citations
• Statistics and Probability — 15 citations into it
• Statistical Learning Theory — 9 citations into it
• Optimization & Training — 8 citations into it
• Causal Inference & Discovery — 7 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it; #11,402 of 21,575 by trust here, #15,529 by citations
• Probabilistic & Bayesian Methods — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Alberto Suárez trusts overall

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

1. Michael I. Jordan
2. Jaka Demsar
3. Jerome H. Friedman
4. Thomas G. Dietterich
5. Leo Breiman
6. John A. Hertz
7. Aapo Hyvärinen
8. Arnold J. Stromberg
9. Anders Krogh
10. Peter Buhlmann

Who trusts Alberto Suárez overall

1. Vinícius G. Costa
2. Ion Iorgulescu
3. Zhi-Hua Zhou
4. Cristina Olaru
5. Peter Kontschieder
6. Ryutaro Tanno
7. Md. Nasim Adnan
8. Carlos Eduardo Pedreira
9. Louis Wehenkel
10. Keith Beven

Papers from Alberto Suárez's perspective

Ordered by how strongly the researchers Alberto Suárez 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. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Non-linear Causal Inference using Gaussianity Measures (2014) — 23 citations
• Gaussianity Measures for Detecting the Direction of Causal Time Series (2011)
• 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
• Globally Optimal Fuzzy Decision Trees for Classification and Regression (1999)