Abstract Deep Neural Networks (DNNs) demonstrated remarkable capabilities in learning
complex hierarchical data representations, but the nature of these representations remains …

In today's AI-driven world, understanding model behavior is becoming more important than
ever. While libraries abound for doing so via traditional XAI methods, the domain of …

Deep Neural Networks (DNNs) are capable of learning complex and versatile
representations, however, the semantic nature of the learned concepts remains unknown. A …

Data slice finding is an emerging technique for validating machine learning (ML) models by
identifying and analyzing subgroups in a dataset that exhibit poor performance, often …

A crucial aspect of understanding the complex nature of Deep Neural Networks (DNNs) is
the ability to explain learned concepts within their latent representations. While various …

Understanding deep models is crucial for deploying them in safety-critical applications. We
introduce GIFT, a framework for deriving post-hoc, global, interpretable, and faithful textual …

Despite their impact on the society, deep neural networks are often regarded as black-box
models due to their intricate structures and the absence of explanations for their decisions …

In the rapidly evolving and increasingly complex field of Artificial Intelligence (AI),
understanding and interpreting the decision‐making process of models is crucial. This …

Efforts to decode neural network vision models necessitate a comprehensive grasp of both
the spatial and semantic facets governing feature responses within images. Most research …