To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

Machine learning has achieved remarkable success in many applications. However,
existing studies are largely based on the closed-world assumption, which assumes that the …

Prompt-based continual learning is an emerging direction in leveraging pre-trained
knowledge for downstream continual learning, and has almost reached the performance …

This survey provides an in-depth analysis of knowledge conflicts for large language models
(LLMs), highlighting the complex challenges they encounter when blending contextual and …

To accommodate real-world dynamics artificial intelligence systems need to cope with
sequentially arriving content in an online manner. Beyond regular Continual Learning (CL) …

This book chapter delves into the dynamics of continual learning, which is the process of
incrementally learning from a non-stationary stream of data. Although continual learning is a …

The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a
significant transformation across multiple industries, as it has facilitated the automation of …

In this work, we study the problem of continual learning (CL) where the goal is to learn a
model on a sequence of tasks, under the assumption that the data from the previous tasks …

Continual learning entails learning a sequence of tasks and balancing their knowledge
appropriately. With limited access to old training samples, much of the current work in deep …

Aluminum flat plate heat pipes have attracted much attention in mobile devices and high-
performance computing due to their lightweight and cost-effectiveness. However, traditional …