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 …

Collecting data is one of the bottlenecks of Human-Computer Interaction (HCI) research.
Motivated by this, we explore the potential of large language models (LLMs) in generating …

Interactive machine learning (IML) is an iterative learning process that tightly couples a
human with a machine learner, which is widely used by researchers and practitioners 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 …

Question classification is a primary essential study for automatic question answering
implementations. Linguistic features take a significant role to develop an accurate question …

Latent spaces—reduced‐dimensionality vector space embeddings of data, fit via machine
learning—have been shown to capture interesting semantic properties and support data …

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 …

With the most advanced natural language processing and artificial intelligence approaches,
effective summarization of long and multi-topic documents—such as academic papers—for …

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

In recent years the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm has
become one of the most used and insightful techniques for exploratory data analysis of high …