3 citations · 7 across the 5 of their papers we have counts for
6 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…
A Neurosymbolic Framework for Bias Correction in Convolutional Neural Networks
Parth Padalkar, Natalia Ślusarz, 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…
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…
Logic of Differentiable Logics: Towards a Uniform Semantics of DL
Natalia Ślusarz, Ekaterina Komendantskaya, Matthew L. Daggitt +2
Differentiable logics (DL) have recently been proposed as a method of training neural networks to satisfy logical specifications. A DL consists of a syntax in which specifications…
Differentiable Logics for Neural Network Training and Verification
Natalia Slusarz, Ekaterina Komendantskaya, Matthew L. Daggitt +1
The rising popularity of neural networks (NNs) in recent years and their increasing prevalence in real-world applications have drawn attention to the importance of their verificati…