3 citations · 3 across the 3 of their papers we have counts for
3 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…
Towards a Certified Proof Checker for Deep Neural Network Verification
Remi Desmartin, Omri Isac, Grant Passmore +3
Recent developments in deep neural networks (DNNs) have led to their adoption in safety-critical systems, which in turn has heightened the need for guaranteeing their safety. These…
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…