arXiv AI
Aug 3
Learning Lookahead Lemmas for Neural Network Verification
arXiv:2607. 29051v1 Announce Type: cross Abstract: State-of-the-art neural network verifiers use the branch-and-bound procedure as their core solving mechanism.
The Luna Bound Propagator for Formal Analysis of Neural Networks
arXiv:2603. 23878v3 Announce Type: replace-cross Abstract: The parameterized CROWN analysis, a.
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arXiv AI
Jul 21
Lookahead Branching for Neural Network Verification
arXiv:2607. 17290v1 Announce Type: cross Abstract: In this work, we investigate the effect of lookahead branching strategies in neural network verification.
Hugging Face Trending Papers
Jul 19
Lookahead Branching for Neural Network Verification
In this work, we investigate the effect of lookahead branching strategies in neural network verification. We present a general recipe to integrate lookahead into any branch-and-bound verifier and demonstrate how one of the current state-of-the-art branching heuristics, FSB, can be viewed as a special instantiation of the lookahead branching strategy.
arXiv AI
Jun 11
\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party
arXiv:2505. 17623v2 Announce Type: replace-cross Abstract: Verifiable computing (VC) has gained prominence in decentralized machine learning systems, where resource-intensive tasks like deep neural network (DNN) inference are offloaded to external participants due to blockchain limitations.
arXiv Machine Learning
Aug 11
Floating-Point Neural Network Verification at the Software Level
arXiv:2510. 23389v2 Announce Type: replace-cross Abstract: The behaviour of neural network components must be proven correct before deployment in safety-critical systems.
arXiv Machine Learning
22h ago
Certified but Private: Scalable Zero-Knowledge Proofs for Neural Network Guarantees
arXiv:2608. 17070v1 Announce Type: new Abstract: With the growing deployment of machine learning models, formal guarantees of the robustness and fairness of these models have become increasingly important in safety-critical and legal-compliance settings.
arXiv AI
Jun 9
TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks
arXiv:2510. 16028v4 Announce Type: replace-cross Abstract: Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces).
arXiv AI
Jun 18
Veriphi: Attack-Guided Neural Network Verification with Dataset-Dependent Training Methods
arXiv:2606. 18454v1 Announce Type: cross Abstract: We present Veriphi, a GPU-accelerated neural network verification system that combines fast adversarial attacks with formal bound certification using alpha,beta-CROWN methods.
arXiv Machine Learning
Jun 4
veriFIRE: an Industrial Case Study in Verifying Consistency Properties for a DNN-Based Wildfire Detection System
arXiv:2606. 04121v1 Announce Type: cross Abstract: We present our ongoing work on the veriFIRE project: a collaboration between industry and academia, aimed at applying verification to increase the reliability of a real-world, safety-critical system.
arXiv AI
Jul 1
Improving Certified Robustness via Adversarial Distillation
arXiv:2606. 31653v1 Announce Type: cross Abstract: Certified training aims to produce models whose predictions can be formally verified against adversarial perturbations, typically by optimising upper bounds on the worst-case loss over an allowed perturbation set.
arXiv Machine Learning
Aug 4
Formally Verifying Analog Neural Networks Under Process Variations Using Polynomial Zonotopes
arXiv:2605. 10474v2 Announce Type: replace Abstract: Analog neural networks are gaining attention due to their efficiency in terms of power consumption and processing speed.
arXiv Machine Learning
5d ago
Branch and Bound for Relational Verification of Neural Networks
arXiv:2608. 13118v1 Announce Type: new Abstract: Verification of neural networks against relational specifications, such as global robustness, is crucial for safety-critical applications of cyber-physical systems (CPS), given their increasing adoption of AI components.