Wouter Verbeke

Artificial Intelligence · 2011–2016

A researcher in the Science corpus: 5 papers; trusts 23 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

Whom Wouter Verbeke trusts overall

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

  1. Bart Baesens
  2. David Martens
  3. Vladimir Naumovich Vapni
  4. William W. Cohen
  5. Thomas G. Dietterich
  6. Chet Langin
  7. Rohan Shah
  8. Jaka Demsar
  9. Dirk Van den Poel
  10. Elen Lima

Who trusts Wouter Verbeke overall

  1. Giulia Vilone
  2. Guido Bologna
  3. Mathieu Lepot
  4. Cynthia Rudin
  5. Alex Alves Freitas
  6. Longo Luca
  7. Fulton Wang
  8. Yoichi Hayashi
  9. Nadia Burkart
  10. David Martens

Papers from Wouter Verbeke's perspective

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

  1. The Nature of Statistical Learning Theory
  2. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
  3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  4. Gradient-based learning applied to document recognition
  5. ImageNet classification with deep convolutional neural networks
  6. Statistical Comparisons of Classifiers over Multiple Data Sets
  7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  8. SMOTE: Synthetic Minority Over-sampling Technique
  9. Et al
  10. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.

Their papers