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Found 10 papers, 2 papers with code
Semirings for Probabilistic and Neuro-Symbolic Logic Programming
The field of probabilistic logic programming (PLP) focuses on integrating probabilistic models into programming languages based on logic.
A Bayesian Unification of Self-Supervised Clustering and Energy-Based Models
Self-supervised learning is a popular and powerful method for utilizing large amounts of unlabeled data, for which a wide variety of training objectives have been proposed in the literature.
Learning Symbolic Representations Through Joint GEnerative and DIscriminative Training
We introduce GEDI, a Bayesian framework that combines existing self-supervised learning objectives with likelihood-based generative models.
Neural Probabilistic Logic Programming in Discrete-Continuous Domains
Probabilistic NeSy focuses on integrating neural networks with both logic and probability theory, which additionally allows learning under uncertainty.
GEDI: GEnerative and DIscriminative Training for Self-Supervised Learning
Our analysis suggests a simple method for integrating self-supervised learning with generative models, allowing for the joint training of these two seemingly distinct approaches.
From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey
This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neurosymbolic and statistical relational artificial intelligence.
DeepStochLog: Neural Stochastic Logic Programming
Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard.
From Statistical Relational to Neuro-Symbolic Artificial Intelligence
Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning.
Neural Probabilistic Logic Programming in DeepProbLog
We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates.
DeepProbLog: Neural Probabilistic Logic Programming
We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates.
