The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

The rapid development of large language models (LLMs) has opened new avenues across
various fields, including cybersecurity, which faces an evolving threat landscape and …

With the prevalence of pre-trained language models (PLMs) and the pre-training–fine-tuning
paradigm, it has been continuously shown that larger models tend to yield better …

Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range
of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always …

The ability of children to ask curiosity-driven questions is an important skill that helps
improve their learning. For this reason, previous research has explored designing specific …

Despite the success, the process of fine-tuning large-scale PLMs brings prohibitive
adaptation costs. In fact, fine-tuning all the parameters of a colossal model and retaining …

Generating realistic human motion from given action descriptions has experienced
significant advancements because of the emerging requirement of digital humans. While …

Transfer learning has recently become the dominant paradigm of machine learning. Pre-
trained models fine-tuned for downstream tasks achieve better performance with fewer …

Advancements in deep neural networks have allowed automatic speech recognition (ASR)
systems to attain human parity on several publicly available clean speech datasets …

Abstract Sign Language Translation (SLT) is a challenging task that aims to translate sign
videos into spoken language. Inspired by the strong translation capabilities of large …