Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

With the continuous growth in the number of parameters of transformer-based pretrained
language models (PLMs), particularly the emergence of large language models (LLMs) with …

Point cloud analysis has achieved outstanding performance by transferring point cloud pre-
trained models. However existing methods for model adaptation usually update all model …

With the rapid development of IT operations, it has become increasingly crucial to efficiently
manage and analyze large volumes of data for practical applications. The techniques of …

A desirable dialog system should be able to continually learn new skills without forgetting
old ones, and thereby adapt to new domains or tasks in its life cycle. However, continually …

In this study, we aim to explore efficient tuning methods for speech self-supervised learning.
Recent studies show that self-supervised learning (SSL) can learn powerful representations …

Large Language Models (LLMs) represent a class of deep learning models adept at
understanding natural language and generating coherent responses to various prompts or …

Content-adaptive compression is crucial for enhancing the adaptability of the pre-trained
neural codec for various contents. Though, its application in neural video compression …

Fine-tuning large-scale pre-trained models is inherently a resource-intensive task. While it
can enhance the capabilities of the model, it also incurs substantial computational costs …

Multilingual machine translation models can benefit from synergy between different
language pairs, but also suffer from interference. While there is a growing number of …