Luís Torgo

Artificial Intelligence · 1997–2019

A researcher in the Science corpus: 9 papers; trusts 76 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #6,273 of 35,345 by trust here, #6,911 by citations
• Statistics and Probability — 23 citations into it
• Information Systems — 20 citations into it; #1,028 of 8,771 by trust here, #5,078 by citations
• Electrical and Electronic Engineering — 19 citations into it
• Evaluation & Benchmarks — 18 citations into it; #696 of 3,147 by trust here, #1,159 by citations
• Computer Vision and Pattern Recognition — 17 citations into it; #5,129 of 21,575 by trust here, #5,754 by citations
• Statistical Learning Theory — 13 citations into it
• Optimization & Training — 10 citations into it; #2,045 of 2,601 by trust here, #2,568 by citations

Whom Luís Torgo trusts overall

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

1. Leo Breiman
2. Robert E. Schapire
3. Yoav Freund
4. Floris Takens
5. Trevor J. Hastie
6. Jerome H. Friedman
7. C. Stone
8. William S. Cleveland
9. Igor Kononenko
10. Robert Tibshirani

Who trusts Luís Torgo overall

1. Wei-Yin Loh
2. Walid Maalej
3. Hugh A. Chipman
4. João Gama
5. Elena Ikonomovska
6. Bernd Bischl
7. Nicola Lunardon
8. Guillaume Lemaitre
9. Joaquin Vanschoren
10. Rina Friedberg

Papers from Luís Torgo's perspective

Ordered by how strongly the researchers Luís Torgo 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• OpenML: networked science in machine learning (2014) — 1,574 citations
• A Survey of Predictive Modeling on Imbalanced Domains (2016) — 1,064 citations
• Evaluating time series forecasting models: an empirical study on performance estimation methods (2019) — 412 citations
• SMOGN: a Pre-processing Approach for Imbalanced Regression (2017) — 309 citations
• Ensembles for Time Series Forecasting (2014) — 81 citations
• SMOTE for Regression (2013) — 308 citations
• Data Mining with R: Learning with Case Studies (2010) — 566 citations
• Predictive models for forecasting hourly urban water demand (2010) — 426 citations
• Functional Models for Regression Tree Leaves (1997)