Infinite numbers of real-world applications use Machine Learning (ML) techniques to
develop potentially the best data available for the users. Transfer learning (TL), one of the …

When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

Large language models (LLMs), exemplified by ChatGPT, have gained considerable
attention for their excellent natural language processing capabilities. Nonetheless, these …

Large language models (LLMs) have gained considerable attention for their excellent
natural language processing capabilities. Nonetheless, these LLMs present many …

Pretrained language models (PLMs) have demonstrated remarkable performance in various
natural language processing tasks: Unidirectional PLMs (eg, GPT) are well known for their …

Few-Shot Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …

With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …

Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …

Multi-task learning (MTL) has been successfully applied to many recommendation
applications. However, MTL models often suffer from performance degeneration with …

Sequential fine-tuning and multi-task learning are methods aiming to incorporate knowledge
from multiple tasks; however, they suffer from catastrophic forgetting and difficulties in …