Julián Luengo

Artificial Intelligence · 2009–2020

A researcher in the Science corpus: 10 papers; trusts 47 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 139 citations into it; #6,378 of 35,345 by trust here, #6,848 by citations
• Information Systems — 57 citations into it; #3,784 of 8,771 by trust here, #2,471 by citations
• Computer Vision and Pattern Recognition — 44 citations into it
• Statistics and Probability — 32 citations into it; #3,674 of 12,932 by trust here, #2,365 by citations
• Evaluation & Benchmarks — 25 citations into it
• Statistical Learning Theory — 24 citations into it
• Computer Networks and Communications — 17 citations into it
• Representation & Self-Supervised Learning — 17 citations into it

Whom Julián Luengo trusts overall

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

1. Salvador Garcia
2. Jaka Demsar
3. Francisco Herrera
4. William W. Cohen
5. Charu C. Aggarwal
6. Jesús Alcalá-Fdez
7. Thomas G. Dietterich
8. Alberto Fernández
9. Nitesh V. Chawla
10. J. Ross Quinlan

Who trusts Julián Luengo overall

1. Salvador Garcia
2. Alberto Fernández
3. Francisco Herrera
4. Isaac Triguero
5. Mikel Galar
6. Verónica Bolón-Canedo
7. Victoria López
8. Joaquín Derrac
9. Hui Wang
10. José A. Sáez

Papers from Julián Luengo's perspective

Ordered by how strongly the researchers Julián Luengo 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power (2010) — 2,151 citations
• Big Data Preprocessing: Enabling Smart Data (2020) — 70 citations
• Tutorial on practical tips of the most influential data preprocessing algorithms in data mining (2016) — 299 citations
• Big data preprocessing: methods and prospects (2016) — 594 citations
• 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
• Data Preprocessing in Data Mining (2014) — 1,068 citations
• A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning (2013) — 525 citations
• KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework (2011) — 2,449 citations
• A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability (2009) — 697 citations