Deep Generative Models with Probabilistic Logic Priors

Misino, Eleonora (2021) Deep Generative Models with Probabilistic Logic Priors. [Laurea magistrale], Università di Bologna, Corso di Studio in Artificial intelligence [LM-DM270]
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Many different extensions of the VAE framework have been introduced in the past. How­ ever, the vast majority of them focused on pure sub­-symbolic approaches that are not sufficient for solving generative tasks that require a form of reasoning. In this thesis, we propose the probabilistic logic VAE (PLVAE), a neuro-­symbolic deep generative model that combines the representational power of VAEs with the reasoning ability of probabilistic ­logic programming. The strength of PLVAE resides in its probabilistic ­logic prior, which provides an interpretable structure to the latent space that can be easily changed in order to apply the model to different scenarios. We provide empirical results of our approach by training PLVAE on a base task and then using the same model to generalize to novel tasks that involve reasoning with the same set of symbols.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Misino, Eleonora
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
deep learning,neuro-symbolic AI,deep generative models,VAE,probabilistic logic programming,ProbLog,reasoning,logic priors
Data di discussione della Tesi
8 Ottobre 2021

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