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Modern Neural Networks Generalize Well On Small Data Sets.pdf

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Preview of Modern Neural Networks Generalize Well on Small Data Sets
🔗 Source: papers.nips.cc
📊 Size: 992 KB
👤 Author: Matthew Olson, Abraham Wyner, Richard Berk
⬇️ Downloads: 68

Summary

Researchers from the University of Pennsylvania found that large neural networks can generalize well on small, noisy data sets, even with hundreds of parameters per training observation. They used a linear program to decompose fitted neural networks into ensembles of low-bias sub-networks, which are relatively uncorrelated, leading to an internal regularization process. This explains why neural networks are resistant to overfitting. The study applied large neural networks to 116 real-world data sets from the UCI Machine Learning Repository and found that they can achieve superior classification accuracy without overfitting, even with minimal tuning. The key takeaways are that large neural networks can generalize well on small data sets, can be trained with minimal tuning, and have a natural interpretation as an ensemble of low-bias classifiers with less than perfect pairwise correlation.

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Researchers from the University of Pennsylvania found that large neural networks can generalize well on small, noisy data sets, even with hundreds of...

Technical Information

  • File Format: PDF
  • File Size: 992 KB
  • Pages: 10
  • Language: EN
  • Author: Matthew Olson, Abraham Wyner, Richard Berk
  • Total Downloads: 68
  • Last Updated: 6 days ago

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