Difference between revisions of "GP SSM"

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(Papers on Deep Learning Theory)
(Papers on Deep Learning Theory)
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** A. Alemi, I. Fischer, K. Dillon, and K. Murphey, [https://arxiv.org/abs/1612.00410 Deep Variational Information Bottleneck], ''arXiV:1612.00410''
 
** 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]
 
** F. Anselmi, L. Rosasco, T. Poggio, [https://arxiv.org/pdf/1503.05938.pdf On Invariance and Selectivity in Representation Learning]
 +
** Y. Bengio [https://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf Learning Deep Architectures for AI], 2009.
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** J. Bruna and S. Mallat, [http://www.cmap.polytechnique.fr/scattering/scattering_cvpr2011.pdf Classification with Scattering Operators], ''CVPR'', 2011.
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** P. Chaudhari, A. Choromanska, S. Soatto, Y. LeCun, C. Baldassi, C. Borgs, J. Chayes, L. Sagun, R. Zecchina [https://arxiv.org/abs/1611.01838 Entropy-SGD: Biasing Gradient Descent into Wide Valleys], ''Proceedings of the International Conference on Learning Representations (ICLR)'', 2017
 
** 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.
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** D.-A. Clevert, T. Unterthiner, and S. Hochreiter, [https://arxiv.org/abs/1511.07289 Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)], ''arXiv:1511.07289''
 
* 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''
 
* 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''
  

Revision as of 21:30, 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