Transformer-based language models (TLMs) have widely been recognized to be a cutting-
edge technology for the successful development of deep-learning-based solutions to …

Humans can learn a new language task efficiently with only few examples, by leveraging
their knowledge obtained when learning prior tasks. In this paper, we explore whether and …

Code review is one of the best practices as a powerful safeguard for software quality. In
practice, senior or highly skilled reviewers inspect source code and provide constructive …

In recent years, the pre-training of Large Language Models (LLMs) in the educational
domain has garnered significant attention. However, a discernible gap exists in the …

The increasing adoption of natural language processing (NLP) models across industries has
led to practitioners' need for machine learning (ML) systems to handle these models …

Precisely predicting molecular properties is crucial in drug discovery, but the scarcity of
labeled data poses a challenge for applying deep learning methods. While large-scale self …

In real-world scenarios, a text classification task often begins with a cold start, when labeled
data is scarce. In such cases, the common practice of fine-tuning pre-trained models, such …

Despite the development of various deep learning methods for Wi-Fi sensing, package loss
often results in noncontinuous estimation of the Channel State Information (CSI), which …

Machine learning algorithms have had a profound impact on the field of computer science
over the past few decades. The performance of these algorithms heavily depends on the …

Machine Learning algorithms have had a profound impact on the field of computer science
over the past few decades. These algorithms performance is greatly influenced by the …