Nour Makke

Artificial Intelligence · 2022

A researcher in the Science corpus: 1 paper; trusts 17 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Nour Makke trusts overall

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

  1. Steven L. Brunton
  2. Michael D. Schmidt
  3. S. Udrescu
  4. R. Beals
  5. Rohit Batra
  6. Kathleen P. Champion
  7. William La Cava
  8. J. Nathan Kutz
  9. Hod Lipson
  10. Max Tegmark

Papers from Nour Makke's perspective

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

  1. Distilling Free-Form Natural Laws from Experimental Data
  2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  3. ImageNet classification with deep convolutional neural networks
  4. Gradient-based learning applied to document recognition
  5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  6. ImageNet: A large-scale hierarchical image database
  7. Discovering governing equations from data by sparse identification of nonlinear dynamical systems
  8. Long Short-Term Memory
  9. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
  10. Regression Shrinkage and Selection via the Lasso

Their papers