Search for: All records

This analysis utilizes a residual 3D convolutional neural network (equipped with 4 attention layers). The core contributions of our work are twofold: firstly, we have innovatively integrated clinical expertise into the initialization of the attention layer’s weights through whole-brain segmentation technique; secondly, we have employed various state-of-the-art model interpretation techniques. These techniques effectively annotate influential brain regions and demonstrate promising results within neuroimaging analysis, as reflected in the key metrics and outcomes. Our findings underscore the potential of deep learning in neuroimaging, especially highlighting the critical role of comprehensive brain segmentation in enhancing diagnostic accuracy. 

Deep Neural Networks (DNNs) have achieved tremendous success in various tasks. However, DNNs exhibit uncertainty and unreliability when faced with well-designed adversarial examples, leading to misclassification. To address this, a variety of methods have been proposed to improve the robustness of DNNs by detecting adversarial attacks. In this paper, we combine model explanation techniques with adversarial models to enhance adversarial detection in real-world scenarios. Specifically, we develop a novel adversary-resistant detection framework called EXPLAINER, which utilizes explanation results extracted from explainable learning models. The explanation model in EXPLAINER generates an explanation map that identifies the relevance of input variables to the model’s classification result. Consequently, adversarial examples can be effectively detected by comparing the explanation results of a given sample with its denoised version, without relying on any prior knowledge of attacks. The proposed framework is thoroughly evaluated against different adversarial attacks, and experimental results demonstrate that our approach achieves promising results in white-box attack scenarios. 

In an era dominated by web-based intelligent customer services, the applications of Sentence Pair Matching are profoundly broad. Web agents, for example, automatically respond to customer queries by finding similar past questions, significantly reducing customer service expenses. While current large language models (LLMs) offer powerful text generation capabilities, they often struggle with opacity, potential text toxicity, and difficulty managing domain-specific and confidential business inquiries. Consequently, the widespread adoption of web-based intelligent customer services in real-world business still greatly relies on query-based interactions. In this paper, we introduce a series of model-agnostic techniques aimed at enhancing both the accuracy and interpretability of Chinese pairwise sentence-matching models. Our contributions include (1) An Edit-distance-weighted fine-tuning method, (2) A Bayesian Iterative Prediction algorithm, (3) A Lexical-based Dual Ranking Interpreter, and (4) A Bi-criteria Denoising strategy. Experimental results on the Large-scale Chinese Question Matching Corpus (LCQMC) with a disturbed test demonstrate that our fine-tuning and prediction methods can steadily improve matching accuracy, building on the current state-of-the-art models. Besides, our interpreter with denoising strategy markedly enhances token-level interpretation in rationality and loyalty. In both matching accuracy and interpretation, our approaches outperform classic methods and even LLMs.