Paulo Cortez

Artificial Intelligence · 2006–2018

A researcher in the Science corpus: 14 papers; trusts 71 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #4,695 of 35,345 by trust here, #13,962 by citations
• Information Systems — 40 citations into it; #754 of 8,771 by trust here, #2,539 by citations
• Evaluation & Benchmarks — 29 citations into it
• Statistics and Probability — 26 citations into it; #5,758 of 12,932 by trust here, #3,703 by citations
• Statistical Learning Theory — 19 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it; #7,548 of 21,575 by trust here, #14,151 by citations
• Management Science and Operations Research — 13 citations into it; #10,704 of 11,946 by trust here, #10,392 by citations
• Sociology and Political Science — 12 citations into it

Whom Paulo Cortez trusts overall

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

1. David Ruppert
2. Mark J. Embrechts
3. R. Kewley
4. Thomas G. Dietterich
5. Vladimir Cherkassky
6. Tom Fawcett
7. Trevor J. Hastie
8. Tingyao Wu
9. Alex Smola
10. Leo Breiman

Who trusts Paulo Cortez overall

1. Sérgio Moro
2. Mark J. Embrechts
3. Amina Adadi
4. Paulo Rita
5. Riccardo Guidotti
6. Matt Olfat
7. Vaishak Belle
8. John Cristian Borges Gamboa
9. Raul M. S. Laureano
10. Mohammed Berrada

Papers from Paulo Cortez's perspective

Ordered by how strongly the researchers Paulo Cortez 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. 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. Long Short-Term Memory
7. The Nature of Statistical Learning Theory
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A data-driven approach to predict the success of bank telemarketing (2014) — 999 citations
• Multi‐scale Internet traffic forecasting using neural networks and time series methods (2010) — 203 citations
• Mortality assessment in intensive care units via adverse events using artificial neural networks (2006) — 76 citations
• Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis (2018) — 302 citations
• Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation (2015) — 305 citations
• A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News (2015) — 231 citations
• Using sensitivity analysis and visualization techniques to open black box data mining models (2013) — 428 citations
• Using data mining for bank direct marketing: an application of the CRISP-DM methodology (2011) — 305 citations
• Opening black box Data Mining models using Sensitivity Analysis (2011) — 113 citations
• Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool (2010) — 259 citations
• Modeling wine preferences by data mining from physicochemical properties (2009) — 1,519 citations
• Using Data Mining for Wine Quality Assessment (2009) — 62 citations
• Using data mining to predict secondary school student performance (2008) — 773 citations
• A data mining approach to predict forest fires using meteorological data (2007) — 460 citations