Poster Open Access

DeepSphere: towards an equivariant graph-based spherical CNN (ICLR'19 RLGM workshop poster)

Defferrard, Michaël; Perraudin, Nathanaël


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  <dc:creator>Defferrard, Michaël</dc:creator>
  <dc:creator>Perraudin, Nathanaël</dc:creator>
  <dc:date>2019-05-06</dc:date>
  <dc:description>Poster presented at the Representation Learning on Graphs and Manifolds (https://rlgm.github.io) workshop at ICLR'19.</dc:description>
  <dc:identifier>https://zenodo.org/record/2839355</dc:identifier>
  <dc:identifier>10.5281/zenodo.2839355</dc:identifier>
  <dc:identifier>oai:zenodo.org:2839355</dc:identifier>
  <dc:relation>arxiv:arXiv:1904.05146</dc:relation>
  <dc:relation>url:https://github.com/SwissDataScienceCenter/DeepSphere</dc:relation>
  <dc:relation>doi:10.5281/zenodo.2839354</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>graph neural networks</dc:subject>
  <dc:subject>geometric deep learning</dc:subject>
  <dc:subject>spherical neural networks</dc:subject>
  <dc:subject>equivariance</dc:subject>
  <dc:title>DeepSphere: towards an equivariant graph-based spherical CNN (ICLR'19 RLGM workshop poster)</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePoster</dc:type>
  <dc:type>poster</dc:type>
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