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Obtaining compositional mappings is important for the model to generalize well compositionally.
“Information theory, inference and learning algorithms”
David MacKay · 2003
Earlier work this paper cites.
“Understanding linguistic evolution by visualizing the emergence of topographic mappings”
Henry Brighton and Simon Kirby · 2006
Earlier work this paper cites.
“Compression and communication in the cultural evolution of linguistic structure”
Simon Kirby, Monica Tamariz, Hannah Cornish and Kenny Smith · 2015
Earlier work this paper cites.
“Deep residual learning for image recognition”
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Earlier work this paper cites.
“dSprites: Disentanglement testing Sprites dataset”, https://github.com/deepmind/dsprites-dataset/, 2017
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Earlier work this paper cites.
“Towards a definition of disentangled representations”, 2018
Irina Higgins, David Amos, David Pfau, Sebastien Racaniere, Loic Matthey, Danilo Rezende and Alexander Lerchner · 2018
Earlier work this paper cites.
“Measuring Compositionality in Representation Learning”
Jacob Andreas · 2019
Earlier work this paper cites.
“The Local Elasticity of Neural Networks”
Hangfeng He and Weijie Su · 2020
Earlier work this paper cites.
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