The past decade has witnessed a plethora of works that leverage the power of visualization
(VIS) to interpret machine learning (ML) models. The corresponding research topic, VIS4ML …

Embeddings mapping high-dimensional discrete input to lower-dimensional continuous
vector spaces have been widely adopted in machine learning applications as a way to …

Neural language models are widely used; however, their model parameters often need to be
adapted to the specific domains and tasks of an application, which is time-and resource …

Modern machine learning techniques commonly rely on complex, high-dimensional
embedding representations to capture underlying structure in the data and improve …

Over the past years, an increasing number of publications in information visualization,
especially within the field of visual analytics, have mentioned the term “embedding” when …

In this work, we propose an interactive visual approach for the exploration and formation of
structural relationships in embeddings of high-dimensional data. These structural …

Graph embedding, which constructs vector representation of nodes in a network, has shown
effectiveness in many graph analysis tasks, such as node classification, node clustering, and …

Word vector embeddings have been shown to contain and amplify biases in the data they
are extracted from. Consequently, many techniques have been proposed to identify …

Graph neural networks (GNNs) are a class of powerful machine learning tools that model
node relations for making predictions of nodes or links. GNN developers rely on quantitative …

Machine learning models often learn latent embedding representations that capture the
domain semantics of their training data. These embedding representations are valuable for …