From the 1 of 6 linked papers with an AI index.
6 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…
VNN-LIB 2.0: Rigorous Foundations for Neural Network Verification
Ann Roy, Allen Antony, Andrea Gimelli +1
Neural network verification is an active and rapidly maturing research area, with a growing ecosystem of solvers and tools. The VNN-LIB standard was introduced to support interoper…
Compiling High-Level Neural Network Specifications into VNN-LIB Queries
Matthew L. Daggitt, Wen Kokke, Robert Atkey
The formal verification of traditional software has been revolutionised by verification-orientated languages such as Dafny and F* which enable developers to write high-level specif…
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…
Neural Network Verification is a Programming Language Challenge
Lucas C. Cordeiro, Matthew L. Daggitt, Julien Girard-Satabin +8
Neural network verification is a new and rapidly developing field of research. So far, the main priority has been establishing efficient verification algorithms and tools, while pr…
NLP Verification: Towards a General Methodology for Certifying Robustness
Marco Casadio, Tanvi Dinkar, Ekaterina Komendantskaya +6
Machine Learning (ML) has exhibited substantial success in the field of Natural Language Processing (NLP). For example large language models have empirically proven to be capable o…