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arXiv.org e-Print Archive
Preprint . 2017
Data sources: arXiv.org e-Print Archive
https://dx.doi.org/10.48550/ar...
Article . 2017
License: arXiv Non-Exclusive Distribution
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Differentiable cellular automata

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2017Embargo end date: 01 Jan 2017Publisher:arXiv
Authors: Martin, Carlos;

doi: 10.48550/arxiv.1708.09546

arXiv: http://arxiv.org/abs/1708.09546

Differentiable cellular automata

Abstract

We describe a class of cellular automata (CAs) that are end-to-end differentiable. DCAs interpolate the behavior of ordinary CAs through rules that act on distributions of states. The gradient of a DCA with respect to its parameters can be computed with an iterative propagation scheme that uses previously-computed gradients and values. Gradient-based optimization over DCAs could be used to find ordinary CAs with desired properties.

Keywords

FOS: Computer and information sciences, Discrete Mathematics (cs.DM), Cellular Automata and Lattice Gases (nlin.CG), FOS: Physical sciences, Nonlinear Sciences - Cellular Automata and Lattice Gases, Computer Science - Discrete Mathematics

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citations
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!
0
Average
Average
Average
Green