The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

Visual analytics for machine learning has recently evolved as one of the most exciting areas
in the field of visualization. To better identify which research topics are promising and to …

Deep learning has recently seen rapid development and received significant attention due
to its state-of-the-art performance on previously-thought hard problems. However, because …

End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to
understand. This has given rise to numerous efforts towards model explainability in recent …

Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

Neural sequence-to-sequence models have proven to be accurate and robust for many
sequence prediction tasks, and have become the standard approach for automatic …

The rapid development of deep natural language processing (NLP) models for text
classification has led to an urgent need for a unified understanding of these models …

Advances in language modeling have led to the development of deep attention-based
models that are performant across a wide variety of natural language processing (NLP) …

While many VA workflows make use of machine-learned models to support analytical tasks,
VA workflows have become increasingly important in understanding and improving Machine …

Horizontal federated learning (HFL) enables distributed clients to train a shared model and
keep their data privacy. In training high-quality HFL models, the data heterogeneity among …