arXiv:2606. 04314v1 Announce Type: new Abstract: As neural networks are increasingly deployed in safety-critical domains, testing is essential to evaluate and improve their reliability.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2606. 04310v1 Announce Type: new Abstract: Deep Neural Networks (DNNs) are increasingly being deployed in security-critical and safety-sensitive applications, which makes rigorous testing essential to identify and mitigate model weaknesses.
arXiv:2607. 20046v1 Announce Type: cross Abstract: With the widespread deployment of deep neural networks (DNNs) in safety-critical domains, reducing the cost of model validation under limited testing budgets has become increasingly important.
arXiv:2604. 23099v2 Announce Type: replace-cross Abstract: Evaluating generative AI models is increasingly resource-intensive due to slow inference, expensive raters, and a rapidly growing landscape of models and benchmarks.
arXiv:2601. 15041v2 Announce Type: replace Abstract: The increasing deployment of deep learning systems requires systematic evaluation of their reliability in real-world scenarios.
arXiv:2608. 12144v1 Announce Type: cross Abstract: Over the past decade, many test adequacy metrics have been proposed for deep learning that characterize test dataset adequacy from different perspectives, e.
arXiv:2607. 05461v1 Announce Type: cross Abstract: Existing methods for testing deep neural networks (DNNs) primarily prioritize test inputs likely to reveal model faults under a fixed labeling budget.