4 citations · 4 across the 4 of their papers we have counts for
4 papers
Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning
Jonathan Feldstein, Paulius Dilkas, Vaishak Belle +1
Integrating symbolic techniques with statistical ones is a long-standing problem in artificial intelligence. The motivation is that the strengths of either area match the weaknesse…
Efficiently Learning Probabilistic Logical Models by Cheaply Ranking Mined Rules
Jonathan Feldstein, Dominic Phillips, Efthymia Tsamoura
Probabilistic logical models are a core component of neurosymbolic AI and are important in their own right for tasks that require high explainability. Unlike neural networks, logic…
Parallel Neurosymbolic Integration with Concordia
Jonathan Feldstein, Modestas Jurčius, Efthymia Tsamoura
Parallel neurosymbolic architectures have been applied effectively in NLP by distilling knowledge from a logic theory into a deep model.However, prior art faces several limitations…
Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models
Jonathan Feldstein, Dominic Phillips, Efthymia Tsamoura
Structure learning is a core problem in AI central to the fields of neuro-symbolic AI and statistical relational learning. It consists in automatically learning a logical theory fr…