Difference between revisions of "GP SSM"

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m (Papers on Deep Learning)
(Papers on Deep Learning Theory)
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* This is the paper that we seek to understand in this reading group.
 
* This is the paper that we seek to understand in this reading group.
 
** A. Achile and S. Soatto, [[Media:InvarianceDisentanglement.pdf | Emergence of Invariance and Disentanglement in Deep Representations]], ''arXiV:1706.01350v2'', Oct. 2017.
 
** A. Achile and S. Soatto, [[Media:InvarianceDisentanglement.pdf | Emergence of Invariance and Disentanglement in Deep Representations]], ''arXiV:1706.01350v2'', Oct. 2017.
* P. Chauderi, A.Oberman, S. Osher, S. Soatto, G. Ccarlier, [[Media:DeepNetPDEs.pdf | Deep Relaxation: Partial Differential Equations for Optimizing Deep Neural Networks]], ''arXiV:1704.04932v2''
 
 
* Here are some background papers
 
* Here are some background papers
 +
** A. Achille and S. Soatto [https://arxiv.org/pdf/1611.01353.pdf  Information Dropout: Learning Optimal Representations Through Noisy Computation]
 +
** A. Alemi, I. Fischer, K. Dillon, and K. Murphey, [https://arxiv.org/abs/1612.00410 Deep Variational Information Bottleneck], ''arXiV:1612.00410''
 +
** F. Anselmi, L. Rosasco, T. Poggio, [https://arxiv.org/pdf/1503.05938.pdf On Invariance and Selectivity in Representation Learning]
 
** N. Tishby and N. Zaslavsky, [[Media:DeepLearningBottleneck.pdf | Deep Learning and the Information Bottleneck Principle]], ''arXiv:1503.02406'', March, 2015.
 
** N. Tishby and N. Zaslavsky, [[Media:DeepLearningBottleneck.pdf | Deep Learning and the Information Bottleneck Principle]], ''arXiv:1503.02406'', March, 2015.
 +
* P. Chauderi, A.Oberman, S. Osher, S. Soatto, G. Ccarlier, [[Media:DeepNetPDEs.pdf | Deep Relaxation: Partial Differential Equations for Optimizing Deep Neural Networks]], ''arXiV:1704.04932v2''
  
 
==== Review-like papers ====
 
==== Review-like papers ====

Revision as of 21:21, 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 Theory

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