250 citations · 573 across the 36 of their papers we have counts for
38 papers
sLTN: Structural Logic Tensor Networks
Davide Rinaldi, Luciano Serafini
Logic Tensor Networks (LTN) provide a neurosymbolic framework in which first-order logic is interpreted through tensor operations, enabling logical constraints to be integrated wit…
Lifted Inference beyond First-Order Logic
Sagar Malhotra, Davide Bizzaro, Luciano Serafini
Weighted First Order Model Counting (WFOMC) is fundamental to probabilistic inference in statistical relational learning models. As WFOMC is known to be intractable in general ($\#…
logLTN: Differentiable Fuzzy Logic in the Logarithm Space
Samy Badreddine, Luciano Serafini, Michael Spranger
The AI community is increasingly focused on merging logic with deep learning to create Neuro-Symbolic (NeSy) paradigms and assist neural approaches with symbolic knowledge. A signi…
Weakly-Supervised Visual-Textual Grounding with Semantic Prior Refinement
Davide Rigoni, Luca Parolari, Luciano Serafini +2
Using only image-sentence pairs, weakly-supervised visual-textual grounding aims to learn region-phrase correspondences of the respective entity mentions. Compared to the supervise…
Interval Logic Tensor Networks
Samy Badreddine, Gianluca Apriceno, Andrea Passerini +1
In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic that interprets knowledge such as sequential properties (traces) and event properties using sequences of r…
Weighted First Order Model Counting with Directed Acyclic Graph Axioms
Sagar Malhotra, Luciano Serafini
Statistical Relational Learning (SRL) integrates First-Order Logic (FOL) and probability theory for learning and inference over relational data. Probabilistic inference and learnin…