In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …

Sample labeling is the most primary and fundamental step of semi-supervised learning. In
literature, most existing methods randomly label samples with a given ratio, but achieve …

Training recommendation models on large datasets requires significant time and resources.
It is desired to construct concise yet informative datasets for efficient training. Recent …

Studies of active learning traditionally assume the target and source data stem from a single
domain. However, in realistic applications, practitioners often require active learning with …

Real-time solutions to the influence blocking maximization (IBM) problems are crucial for
promptly containing the spread of misinformation. However, achieving this goal is non-trivial …

Active learning aims to ease the burden of collecting large amounts of annotated data by
intelligently acquiring labels during the learning process that will be most helpful to learner …

Designing machine learning models followed by their deployment in a real-world
environment has been an area of recent pursuits, resulting in a large number of successful …

Graph-based collaborative filtering has emerged as a powerful paradigm for delivering
personalized recommendations. Despite their demonstrated effectiveness, these methods …

In the decade, researchers have proposed many remarkable algorithms in structural design,
training modes, etc., in the field of Generative AI. However, with the explosive growth of …

Undoubtedly, machine learning (ML) has demonstrated a wealth of far-reaching successes
present both at the level of fundamental developments, design methodologies and …