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.
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.
Ranked #3 on Link Property Prediction on ogbl-biokg
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.