Subtractive Mixture Models via Squaring: Representation and Learning

2 code implementations • 1 Oct 2023 • Lorenzo Loconte, Aleksanteri M. Sladek, Stefan Mengel, Martin Trapp, Arno Solin, Nicolas Gillis, Antonio Vergari

Mixture models are traditionally represented and learned by adding several distributions as components.

How to Turn Your Knowledge Graph Embeddings into Generative Models

1 code implementation • NeurIPS 2023 • Lorenzo Loconte, Nicola Di Mauro, Robert Peharz, Antonio Vergari

Some of the most successful knowledge graph embedding (KGE) models for link prediction -- CP, RESCAL, TuckER, ComplEx -- can be interpreted as energy-based models.

Knowledge Graph Embedding Knowledge Graph Embeddings +1

DeeProb-kit: a Python Library for Deep Probabilistic Modelling

1 code implementation • 8 Dec 2022 • Lorenzo Loconte, Gennaro Gala

DeeProb-kit is a unified library written in Python consisting of a collection of deep probabilistic models (DPMs) that are tractable and exact representations for the modelled probability distributions.