A reusable framework generates verification instances with provably known robustness labels, revealing numeric tolerance issues and bugs in five verifiers while introducing difficulty profiles to diagnose failure modes.
A systematic review of robustness in deep learning for computer vision: Mind the gap?, 2022
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An interdisciplinary workshop produced a catalog of ideas and a roadmap showing how meta-research can tackle challenges like reproducibility, transparency, and ethical implementation in trustworthy AI for healthcare.
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Stress-Testing Neural Network Verifiers with Provably Robust Instances
A reusable framework generates verification instances with provably known robustness labels, revealing numeric tolerance issues and bugs in five verifiers while introducing difficulty profiles to diagnose failure modes.
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Advancing Trustworthy AI in Healthcare Through Meta-Research: Results of an Interdisciplinary Design-Thinking Workshop
An interdisciplinary workshop produced a catalog of ideas and a roadmap showing how meta-research can tackle challenges like reproducibility, transparency, and ethical implementation in trustworthy AI for healthcare.