52 citations · 145 across the 52 of their papers we have counts for
56 papers
Quantitative Linear Logic for Neuro-Symbolic Learning and Verification
Thomas Flinkow, Ekaterina Komendantskaya, Matteo Capucci +1
Differentiable Logics are deployed in neuro-symbolic learning tasks as a way of embedding logical constraints in the training objective of neural networks. A differentiable logic c…
Adequate Losses via Quantitative Linear Logic
Matteo Capucci, Robert Atkey, Charles Grellois +2
As neural components are increasingly embedded in existing symbolic software -- including safety-critical systems -- the question arises of how to specify and enforce the safety of…
Compositional Neural-Cyber-Physical System Verification in the Interactive Theorem Prover of Your Choice
Matthew L. Daggitt, Ekaterina Komendantskaya, Alistair Sirman +4
Formal verification of neuro-symbolic cyber-physical systems, such as drones, medical devices and robots, is complicated. Neural components must be trained to be optimal with respe…
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 General Framework for Property-Driven Machine Learning
Thomas Flinkow, Marco Casadio, Colin Kessler +2
Neural networks have been shown to frequently fail to learn critical safety and correctness properties purely from data, highlighting the need for training methods that directly in…
Neural Network Verification for Gliding Drone Control: A Case Study
Colin Kessler, Ekaterina Komendantskaya, Marco Casadio +5
As machine learning is increasingly deployed in autonomous systems, verification of neural network controllers is becoming an active research domain. Existing tools and annual veri…