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 …

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 …

Abstract The Segment Anything Model (SAM), a foundation model for general image
segmentation, has demonstrated impressive zero-shot performance across numerous …

Transformer-based language models (LMs) are powerful and widely-applicable tools, but
their usefulness is constrained by a finite context window and the expensive computational …

Conventional task-and modality-specific artificial intelligence (AI) models are inflexible in
real-world deployment and maintenance for biomedicine. At the same time, the growing …

In this work, we leverage pre-trained Large Language Models (LLMs) to enhance time-
series forecasting. Mirroring the growing interest in unifying models for Natural Language …

Fine-tuning large pre-trained models has become the de facto strategy for developing both
task-specific and general-purpose machine learning systems, including developing models …

Large language models (LLMs) have garnered unprecedented advancements across
diverse fields, ranging from natural language processing to computer vision and beyond …

We introduce Adapters, an open-source library that unifies parameter-efficient and modular
transfer learning in large language models. By integrating 10 diverse adapter methods into a …

Sequential recommendation aims to predict the subsequent items matching user preference
based on her/his historical interactions. With the development of Large Language Models …