4 papers
A Foundation for Differentiable Logics using Dependent Type Theory
Reynald Affeldt, Alessandro Bruni, Ekaterina Komendantskaya +2
Differentiable logics are a family of quantitative logics originated in the machine learning literature. Because of their origin, differentiable logics often come equipped with ana…
Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs
Matthew L. Daggitt, Wen Kokke, Robert Atkey +3
Neuro-symbolic programs, i.e. programs containing both machine learning components and traditional symbolic code, are becoming increasingly widespread. Finding a general methodolog…
A Neurosymbolic Framework for Bias Correction in Convolutional Neural Networks
Parth Padalkar, Natalia Ålusarz, Ekaterina Komendantskaya +1
Recent efforts in interpreting Convolutional Neural Networks (CNNs) focus on translating the activation of CNN filters into a stratified Answer Set Program (ASP) rule-sets. The CNN…
Taming Differentiable Logics with Coq Formalisation
Reynald Affeldt, Alessandro Bruni, Ekaterina Komendantskaya +2
For performance and verification in machine learning, new methods have recently been proposed that optimise learning systems to satisfy formally expressed logical properties. Among…