3 citations · 7 across the 7 of their papers we have counts for
10 papers
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…
Probabilistic Reasoning at Scale: Trigger Graphs to the Rescue
Efthymia Tsamoura, Jaehun Lee, Jacopo Urbani
The role of uncertainty in data management has become more prominent than ever before, especially because of the growing importance of machine learning-driven applications that pro…
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…
Scalable Regularization of Scene Graph Generation Models using Symbolic Theories
Davide Buffelli, Efthymia Tsamoura
Several techniques have recently aimed to improve the performance of deep learning models for Scene Graph Generation (SGG) by incorporating background knowledge. State-of-the-art t…
Materializing Knowledge Bases via Trigger Graphs
Efthymia Tsamoura, David Carral, Enrico Malizia +1
The chase is a well-established family of algorithms used to materialize Knowledge Bases (KBs), like Knowledge Graphs (KGs), to tackle important tasks like query answering under de…
Neural-Symbolic Integration: A Compositional Perspective
Efthymia Tsamoura, Loizos Michael
Despite significant progress in the development of neural-symbolic frameworks, the question of how to integrate a neural and a symbolic system in a \emph{compositional} manner rema…