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ZENODO
Dataset . 2017
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2017
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2017
License: CC BY
Data sources: Datacite
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Spectral Ergodicity in Deep Learning Architectures via Surrogate Random Matrices

Research datakeyboard_double_arrow_right Dataset 03 Jul 2017Publisher:Zenodo
Authors: Suezen, Mehmet;

doi: 10.5281/zenodo.822410 , 10.5281/zenodo.822411

Spectral Ergodicity in Deep Learning Architectures via Surrogate Random Matrices

Abstract

The new methodology combines approaches inspired from the Thirumalai-Mountain (TM) metric and metric rooted in Kullbach-Leibler (KL) divergence. The method is applied to a general study of deep and recurrent neural networks via the analysis of random matrix ensembles mimicking typical weight matrices of those systems. In particular, we examine different size circular random matrix ensembles: circular unitary ensemble (CUE), circular orthogonal ensemble (COE), and circular symplectic ensemble (CSE). Eigenvalue spectra and spectral ergodicity are computed for those ensembles as a function of connectivity in the system. The dataset and Python notebook are provided.

Notebook works with Python 2.7

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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