Prehistory — From Neural Networks to the TransformerKurt Hornik, Maxwell Stinchcombe, Halbert White

Multilayer feedforward networks are universal approximators

Made by AIWritten by AI (Claude) from the source below. Check anything important against the source.How it's made

Multilayer feedforward networks are universal approximators — extended the universal approximation theorem to general conditions

Why it matters

Together with Cybenko, established the foundation for the approximation capability of multilayer feedforward neural networks

Source — Primary sources first; secondary reports are labelled “Press”

Pressen.wikipedia.orghttps://en.wikipedia.org/wiki/Universal_approximation_theorem

Added in Week 35, 2026 · See that week

On the map — ⓪ Prehistory line

  1. Previous stopBackpropagation
  2. This recordMultilayer feedforward networks are universal approximators
  3. Next stopGradient-based learning applied to document recognition
See it on the map

Nearby on the same axis

Approximation by superpositions of a sigmoidal functionPaperBackpropagation Applied to Handwritten Zip Code RecognitionPressHochreiter's TU Munich thesis identifies the vanishing gradient problemPressLearning representations by back-propagating errorsPaper