Abstract We introduce Iterated Integrated Attributions (IIA)-a generic method for explaining
the predictions of vision models. IIA employs iterative integration across the input image, the …

Transformer-based language models significantly advanced the state-of-the-art in many
linguistic tasks. As this revolution continues, the ability to explain model predictions has …

This paper presents Deep Integrated Explanations (DIX)-a universal method for explaining
vision models. DIX generates explanation maps by integrating information from the …

Two important challenges in recommender systems are modeling users with heterogeneous
taste and providing explainable recommendations. In order to improve our understanding of …

Artificial intelligence and its derivative technologies are not only playing a role in the fields of
medicine, economy, policing, transportation, and natural science computing today but also …

Recently, there has been growing interest in the ability of Transformer-based models to
produce meaningful embeddings of text with several applications, such as text similarity …

We present Learning to Explain (LTX), a model-agnostic framework designed for providing
post-hoc explanations for vision models. The LTX framework introduces an “explainer” …

In the field of recommender systems, explainability remains a pivotal yet challenging aspect.
To address this, we introduce the Learning to eXplain Recommendations (LXR) framework …

We introduce Stochastic Integrated Explanations (SIX)-a general method for explaining
predictions made by vision models. SIX employs stochastic integration on the internal …

Convolutional Neural Networks (CNNs) are commonly used in computer vision. However,
their predictions are difficult to explain, as is the case with many deep learning models. To …