_:nd0f9fdf512384c7db901649ad3ae7589xb0
<http://schema.org/description>
"Deep learning has been shown to achieve impressive results in several tasks\nwhere a large amount of training data is available. However, deep learning\nsolely focuses on the accuracy of the predictions, neglecting the reasoning\nprocess leading to a decision, which is a major issue in life-critical\napplications. Probabilistic logic reasoning allows to exploit both statistical\nregularities and specific domain expertise to perform reasoning under\nuncertainty, but its scalability and brittle integration with the layers\nprocessing the sensory data have greatly limited its applications. For these\nreasons, combining deep architectures and probabilistic logic reasoning is a\nfundamental goal towards the development of intelligent agents operating in\ncomplex environments. This paper presents Relational Neural Machines, a novel\nframework allowing to jointly train the parameters of the learners and of a\nFirst–Order Logic based reasoner. A Relational Neural Machine is able to\nrecover both classical learning from supervised data in case of pure\nsub-symbolic learning, and Markov Logic Networks in case of pure symbolic\nreasoning, while allowing to jointly train and perform inference in hybrid\nlearning tasks. Proper algorithmic solutions are devised to make learning and\ninference tractable in large-scale problems. The experiments show promising\nresults in different relational tasks."
<https://deepai.org/publication/relational-neural-machines>