From the 1 of 10 linked papers with an AI index.
10 papers
Adequate Losses via Quantitative Linear Logic
Matteo Capucci, Robert Atkey, Charles Grellois +2
The paper proposes a family of quantitative linear logics (pQLL) that combine logical specifications with differentiable loss functions for neural components, proving cut‑eliminati…
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…
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…
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…
A Certified Proof Checker for Deep Neural Network Verification in Imandra
Remi Desmartin, Omri Isac, Grant Passmore +3
Recent advances in the verification of deep neural networks (DNNs) have opened the way for a broader usage of DNN verification technology in many application areas, including safet…