Single-cell datasets are routinely collected to investigate changes in cellular state between
control cells and the corresponding cells in a treatment condition, such as exposure to a …

Interpretability methods are valuable only if their explanations faithfully describe the
explained model. In this work, we consider neural networks whose predictions are invariant …

Understanding the severity of conditions shown in images in medical diagnosis is crucial
serving as a key guide for clinical assessment treatment as well as evaluating longitudinal …

Unsupervised representation learning has become an important ingredient of today's deep
learning systems. However, only a few methods exist that explain a learned vector …

Objective: An applied problem facing all areas of data science is harmonizing data sources.
Joining data from multiple origins with unmapped and only partially overlap** features is a …

In modern machine learning, the trend of harnessing self-supervised learning to derive high-
quality representations without label dependency has garnered significant attention …

While the concept of Explainability has advanced significantly in the past decade, many
areas remain unexplored. Although XAI implementations have historically been employed in …

Self-supervised models create representation spaces that lack clear semantic meaning. This
interpretability problem of representations makes traditional explainability methods …

A fundamental barrier to the adoption of AI systems in clinical practice is the insufficient
transparency of AI decision-making. The field of Explainable Artificial Intelligence (XAI) …

Despite the widespread utilization of post-hoc explanation methods for graph neural
networks (GNNs) in high-stakes settings, there has been a lack of comprehensive evaluation …