José A. Sáez

Artificial Intelligence · 2012–2015

A researcher in the Science corpus: 4 papers; trusts 24 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #12,942 of 35,345 by trust here, #15,004 by citations
• Statistical Learning Theory — 13 citations into it
• Statistics and Probability — 11 citations into it
• Information Systems — 10 citations into it; #8,322 of 8,771 by trust here, #7,107 by citations
• Evaluation & Benchmarks — 9 citations into it
• Algorithms & Complexity — 8 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom José A. Sáez trusts overall

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

1. Salvador Garcia
2. William W. Cohen
3. Jaka Demsar
4. Andrew P. Bradley
5. Nitesh V. Chawla
6. Eibe Frank
7. Corinna Cortes
8. Taeho C. Jo
9. Tomasz Maciejewski
10. Alberto Fernández

Who trusts José A. Sáez overall

1. Octavio Loyola-González
2. Alberto Fernández
3. Salvador Garcia
4. Verónica Bolón-Canedo
5. Francisco Herrera
6. Christoph Bergmeir
7. Xin He

Papers from José A. Sáez's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. SMOTE: Synthetic Minority Over-sampling Technique
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. The use of the area under the ROC curve in the evaluation of machine learning algorithms
10. Long Short-Term Memory

Their papers

• SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering (2015) — 583 citations
• On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification (2014) — 85 citations
• A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning (2013) — 525 citations
• Study on the Impact of Partition-Induced Dataset Shift on $k$-Fold Cross-Validation (2012) — 313 citations