32 citations · 53 across the 10 of their papers we have counts for
10 papers
How to Turn Your Knowledge Graph Embeddings into Generative Models
Lorenzo Loconte, Nicola Di Mauro, Robert Peharz +1
Some of the most successful knowledge graph embedding (KGE) models for link prediction -- CP, RESCAL, TuckER, ComplEx -- can be interpreted as energy-based models. Under this persp…
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks
Alejandro Molina, Antonio Vergari, Karl Stelzner +5
We introduce SPFlow, an open-source Python library providing a simple interface to inference, learning and manipulation routines for deep and tractable probabilistic models called…
Sum-Product Networks for Hybrid Domains
Alejandro Molina, Antonio Vergari, Nicola Di Mauro +3
While all kinds of mixed data -from personal data, over panel and scientific data, to public and commercial data- are collected and stored, building probabilistic graphical models…
Towards Representation Learning with Tractable Probabilistic Models
Antonio Vergari, Nicola Di Mauro, Floriana Esposito
Probabilistic models learned as density estimators can be exploited in representation learning beside being toolboxes used to answer inference queries only. However, how to extract…
Visualizing and Understanding Sum-Product Networks
Antonio Vergari, Nicola Di Mauro, Floriana Esposito
Sum-Product Networks (SPNs) are recently introduced deep tractable probabilistic models by which several kinds of inference queries can be answered exactly and in a tractable time.…
Ensemble Relational Learning based on Selective Propositionalization
Nicola Di Mauro, Floriana Esposito
Dealing with structured data needs the use of expressive representation formalisms that, however, puts the problem to deal with the computational complexity of the machine learning…