With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …

In this paper we introduce a novel, unified, open-source model interpretability library for
PyTorch [12]. The library contains generic implementations of a number of gradient and …

Pruning the parameters of deep neural networks has generated intense interest due to
potential savings in time, memory and energy both during training and at test time. Recent …

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. It is thus
imperative to devise effective attack algorithms to identify the deficiencies of DNNs …

The recent release of large-scale healthcare datasets has greatly propelled the research of
data-driven deep learning models for healthcare applications. However, due to the nature of …

Despite rapid adoption and deployment of large language models (LLMs), the internal
computations of these models remain opaque and poorly understood. In this work, we seek …

Image classification models can depend on multiple different semantic attributes of the
image. An explanation of the decision of the classifier needs to both discover and visualize …

In this review, we examine the problem of designing interpretable and explainable machine
learning models. Interpretability and explainability lie at the core of many machine learning …

Combining different models is a widely used paradigm in machine learning applications.
While the most common approach is to form an ensemble of models and average their …