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Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness The proposed CLEVER score is attack-agnostic and is computationally feasible for large neural networks
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579 In this paper, we have proposed a novel counter- factual framework CLEVER for debiasing fact- checking models Unlike existing works, CLEVER is augmentation-free and mitigates biases on infer- ence stage In CLEVER, the claim-evidence fusion model and the claim-only model are independently trained to capture the corresponding information
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te the CLEVER scores for the same set of images and attack targets To the best of our knowledge, CLEVER is the first attack-independent robustness score that is capable of handling the large networks studied in this paper, so we directly r `2 and `1 norms, and Figure 4 visualizes the results for `1 norm Similarly, Table 2 comp
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The integrity of AI benchmarks is fundamental to accurately assess the capabilities of AI systems The internal validity of these benchmarks - i e , making sure they are free from confounding
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Fully characterizing the spatial-spectral priors of hyperspectral images (HSIs) is crucial for HSI denoising tasks Recently, HSI denoising models based on representative coefficient images (RCIs) under the spectral low-rank decomposition framework have garnered significant attention due to their clever utilization of spatial-spectral information in HSI at a low cost However, current methods
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Leaving the barn door open for Clever Hans: Simple features predict LLM benchmark answers Lorenzo Pacchiardi, Marko Tesic, Lucy G Cheke, Jose Hernandez-Orallo 27 Sept 2024 (modified: 05 Feb 2025) Submitted to ICLR 2025 Readers: Everyone
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