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Found 7 papers, 3 papers with code
How to Turn Your Knowledge Graph Embeddings into Generative Models
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
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks
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 (SPNs).
Extremely Randomized CNets for Multi-label Classification
Promising approaches for MLC are those able to capture label dependencies by learning a single probabilistic model—differently from other competitive approaches requiring to learn many models.
Sum-Product Networks for Hybrid Domains
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 for these hybrid domains becomes more difficult.
Visualizing and Understanding Sum-Product Networks
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.
Towards Representation Learning with Tractable Probabilistic Models
Probabilistic models learned as density estimators can be exploited in representation learning beside being toolboxes used to answer inference queries only.
Ensemble Relational Learning based on Selective Propositionalization
Many statistical relational learning approaches try to deal with these problems by combining the construction of relevant relational features with a probabilistic tool.
