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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-10252018-114559


Tipo di tesi
Tesi di laurea magistrale
Autore
COLOMBO, MICHELE
URN
etd-10252018-114559
Titolo
Conditional Variational Auto-Encoders for Tree-Structured data
Dipartimento
INFORMATICA
Corso di studi
INFORMATICA
Relatori
relatore Bacciu, Davide
Parole chiave
  • Deep Learning
  • Machine Learning
  • Tree structured data
  • Tree trasduction
  • Variational Autoencoder
Data inizio appello
30/11/2018
Consultabilità
Completa
Riassunto
The thesis deals with the design of a deep learning model that can learn a generative process realizing unconstrained tree transductions. The model is based on an extension of the popular Variational Autoencoder framework to allow conditioning the generative process on tree structured inputs and to generate tree-structured predictions. It has been realized an efficient Tensorlow implementation of the proposed model, which has been validated on Arithmetic Expression trees and Neural Machine Translation
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