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Found 4 papers, 4 papers with code
Neuro-symbolic computing with spiking neural networks
Knowledge graphs are an expressive and widely used data structure due to their ability to integrate data from different domains in a sensible and machine-readable way.
An energy-based model for neuro-symbolic reasoning on knowledge graphs
Machine learning on graph-structured data has recently become a major topic in industry and research, finding many exciting applications such as recommender systems and automated theorem proving.
Machine learning on knowledge graphs for context-aware security monitoring
Machine learning techniques are gaining attention in the context of intrusion detection due to the increasing amounts of data generated by monitoring tools, as well as the sophistication displayed by attackers in hiding their activity.
SpikE: spike-based embeddings for multi-relational graph data
We propose a spike-based algorithm where nodes in a graph are represented by single spike times of neuron populations and relations as spike time differences between populations.
