Learning latent permutations with Gumbel-Sinkhorn networks
Permutations and matchings are core building blocks in a variety of latent variable models, as they allow us to align, canonicalize, and sort data. Learning in such models is difficult, however, because exact marginalization over these combinatorial objects is intractable. In response, this paper in...
| Autores principales: | , , , |
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| Formato: | Conference item |
| Lenguaje: | English |
| Publicado: |
OpenReview
2018
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