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We have decided to discontinue the publication of preprints on our preprint server end of 2024. The publication culture within mathematics has changed so much due to the rise of repositories such as ArXiV (www.arxiv.org) that we are encouraging all institute members to make their preprints available there. An institute's repository in its previous form is, therefore, unnecessary. The preprints published to date will remain available here, but we will not add any new preprints here.
Maximal Information Divergence from Statistical Models defined by Neural Networks
Guido Montúfar, Johannes Rauh and Nihat AyAbstract
We review recent results about the maximal values of the Kullback-Leibler information divergence from statistical models defined by neural networks, including naïve Bayes models, restricted Boltzmann machines, deep belief networks, and various classes of exponential families. We illustrate approaches to compute the maximal divergence from a given model starting from simple sub- or super-models. We give a new result for deep and narrow belief networks with finite-valued units.
- Received:
- 04.03.13
- Published:
- 04.03.13
- MSC Codes:
- 94A17, 62B10
- Keywords:
- neural network, exponential family, kullback-leibler divergence, multi-information
Related publications
Maximal information divergence from statistical models defined by neural networks
In: Geometric science of information : first international conference, GSI 2013, Paris, France, August 28-30, 2013. Proceedings / Frank Nielsen... (eds.)Berlin [u. a.] : Springer, 2013. - pp. 759-766
(Lecture notes in computer science ; 8085)