Polar: A polynomial arithmetic framework for verifying neural-network controlled systems
We present POLAR (The source code can be found at https://github. com/ChaoHuang2018/
POLAR_Tool. The full version of this paper can be found at https://arxiv …
POLAR_Tool. The full version of this paper can be found at https://arxiv …
pynever: A framework for learning and verification of neural networks
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PRIMA: general and precise neural network certification via scalable convex hull approximations
Formal verification of neural networks is critical for their safe adoption in real-world
applications. However, designing a precise and scalable verifier which can handle different …
applications. However, designing a precise and scalable verifier which can handle different …
[HTML][HTML] An abstraction-based framework for neural network verification
Deep neural networks are increasingly being used as controllers for safety-critical systems.
Because neural networks are opaque, certifying their correctness is a significant challenge …
Because neural networks are opaque, certifying their correctness is a significant challenge …
Reachnn: Reachability analysis of neural-network controlled systems
Applying neural networks as controllers in dynamical systems has shown great promises.
However, it is critical yet challenging to verify the safety of such control systems with neural …
However, it is critical yet challenging to verify the safety of such control systems with neural …
Reachnn*: A tool for reachability analysis of neural-network controlled systems
We introduce ReachNN*, a tool for reachability analysis of neural-network controlled
systems (NNCSs). The theoretical foundation of ReachNN* is the use of Bernstein …
systems (NNCSs). The theoretical foundation of ReachNN* is the use of Bernstein …
[HTML][HTML] Sparse polynomial optimisation for neural network verification
M Newton, A Papachristodoulou - Automatica, 2023 - Elsevier
The prevalence of neural networks in applications is expanding at an increasing rate. It is
becoming clear that providing robust guarantees on systems that use neural networks is …
becoming clear that providing robust guarantees on systems that use neural networks is …
Automatic abstraction refinement in neural network verification using sensitivity analysis
The formal verification of neural networks is essential for their application in safety-critical
environments. However, the set-based verification of neural networks using linear …
environments. However, the set-based verification of neural networks using linear …
Piecewise linear neural networks verification: A comparative study
The success of Deep Learning and its potential use in many important safety-critical
applications has motivated research on formal verification of Neural Net-work (NN) models …
applications has motivated research on formal verification of Neural Net-work (NN) models …
Verification of neural-network control systems by integrating Taylor models and zonotopes
C Schilling, M Forets, S Guadalupe - … of the AAAI Conference on Artificial …, 2022 - ojs.aaai.org
We study the verification problem for closed-loop dynamical systems with neural-network
controllers (NNCS). This problem is commonly reduced to computing the set of reachable …
controllers (NNCS). This problem is commonly reduced to computing the set of reachable …