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This paper establishes a theoretical framework showing that smooth activations in deep neural networks can mitigate the curse of dimensionality in uniform convergence, providing non-asymptotic guarantees and outperforming ReLU networks in worst-case reliability.
This paper presents a Marchenko-Pastur random matrix approach to pruning deep neural networks, offering theoretical guarantees and achieving strong accuracy retention with minimal fine-tuning on ImageNet for ViT and CNN architectures.
A mathematically principled framework, Variational Inference Evidential Deep Learning (VI-EDL), is proposed to address limitations in conventional Evidential Deep Learning by reformulating it through variational inference, deriving an Evidence Lower Bound, establishing a generalization bound, and achieving state-of-the-art performance on visual and medical datasets.
This paper introduces CAFD, a learning-based approach for DNN fault detection that integrates model-based, distance-based, and a novel concept-based feature called Concept Failure Ratio (CFR) derived from Vision-Language Models. CAFD consistently outperforms state-of-the-art baselines in fault detection rate across multiple datasets and budgets.
This paper presents a comprehensive analysis of the Neural Tangent Generalization Attack (NTGA) for data protection, including a taxonomy of related attacks, and discusses future research directions.