Large language models (LLMs) have demonstrated impressive capabilities in natural
language processing. However, their internal mechanisms are still unclear and this lack of …

Integrated Gradients (IG) is a commonly used feature attribution method for deep neural
networks. While IG has many desirable properties, the method often produces …

As a prominent attribution-based explanation algorithm, Integrated Gradients (IG) is widely
adopted due to its desirable explanation axioms and the ease of gradient computation. It …

The surge in black-box AI models has prompted the need to explain the internal mechanism
and justify their reliability, especially in high-stakes applications, such as healthcare and …

AI explainability improves the transparency and trustworthiness of models. However, in the
domain of images, where deep learning has succeeded the most, explainability is still poorly …

Bias in the training data can jeopardize fairness and explainability of deep neural network
prediction on test data. We propose a novel bias-tailored data augmentation approach …

Abstract Neural SDEs with Brownian motion as noise lead to smoother attributions than
traditional ResNets. Various attribution methods such as saliency maps, integrated …

Nanophotonic devices excel at confining light into intense hot spots of electromagnetic near
fields, creating exceptional opportunities for light–matter coupling and surface-enhanced …

There are increasing demands for understanding deep neural networks'(DNNs) behavior
spurred by growing security and/or transparency concerns. Due to multi-layer nonlinearity of …

Explanation methods analyze the features in backdoored input data that contribute to model
misclassification. However, current methods like path techniques struggle to detect backdoor …