Difference between revisions of "GP SSM"

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''Scattering Networks'' are proposed by Stephan Mallat (of wavelet fame) to understand why deep nets work so well.
 
''Scattering Networks'' are proposed by Stephan Mallat (of wavelet fame) to understand why deep nets work so well.
 
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==== Classics ====
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* K. Hornik, M. Stinchcombe, H. White, [[Media:HSH.pdf | Universal Approximation of an Unknown Mapping and Its Derivatives Using Multi-Layer Feedforward Networks]], ''Neural networks'', vol. 3, 1990.
  
 
=== Web Links ===
 
=== Web Links ===

Revision as of 18:33, 16 January 2018

This page gathers references and materials related to the study of

  • Gaussian Process (GP) State Space Models (SSM)
  • Deep Learning
  • Koopman Spectral Methods.

Gaussian Process Approaches

Basic Gaussian Process Info

  • Rasmussen and Williams

Web Links

Papers on GP-SSMs


Deep Learning

Papers on Deep Learning

Review-like papers

Papers on Scattering Networks

Scattering Networks are proposed by Stephan Mallat (of wavelet fame) to understand why deep nets work so well.

Classics

Web Links

Koopman Spectral Method

Papers on Koopman Spectral methods

Papers which are more oriented toward control

Papers which are more oriented toward fluids

Some Early Papers

Other Papers