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Evaluating the Robustness of Neural Networks: An Extreme Value. . . 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
Counterfactual Debiasing for Fact Verification 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
Leaving the barn door open for Clever Hans: Simple features predict. . . This phenomenon, widely known in human and animal experiments, is often referred to as the 'Clever Hans' effect, where tasks are solved using spurious cues, often involving much simpler processes than those putatively assessed Previous research suggests that language models can exhibit this behaviour as well
On the Planning Abilities of Large Language Models : A Critical . . . While, as we mentioned earlier, there can be thorny “clever hans” issues about humans prompting LLMs, an automated verifier mechanically backprompting the LLM doesn’t suffer from these We tested this setup on a subset of the failed instances in the one-shot natural language prompt configuration using GPT-4, given its larger context window
From Control Application to Control Logic: PLC Decompile Framework. . . To address the challenge, we propose a PLC decompile framework named CLEVER, which can analyze the control application and extract the control logic First, we propose a simulation execution based code extraction method, which is utilized to filter the control logic related data
EVALUATING THE ROBUSTNESS OF NEURAL NET : A E VALUE THEORY APPROACH 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
Learnable Representative Coefficient Image Denoiser for. . . 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
LENFusion: A Joint Low-Light Enhancement and Fusion Network for. . . The enhancement is performed in two stages In the initial stage, LAN applies adaptive luminance adjustment to the original visible image Subsequently, RFN achieves secondary enhancement and feature fusion with a clever combination of dual-attention mechanism, which motivates the fusion results to have high contrast and sharpness