Continual Learning (CL) aims to sequentially train models on streams of incoming data that
vary in distribution by preserving previous knowledge while adapting to new data. Current …

Large language models (LLMs) are routinely pre-trained on billions of tokens, only to start
the process over again once new data becomes available. A much more efficient solution is …

In continual learning, plasticity refers to the ability of an agent to quickly adapt to new
information. Neural networks are known to lose plasticity when processing non-stationary …

Continual learning is a sub-field of machine learning, which aims to allow machine learning
models to continuously learn on new data, by accumulating knowledge without forgetting …

An agent that efficiently accumulates knowledge to develop increasingly sophisticated skills
over a long lifetime could advance the frontier of artificial intelligence capabilities. The …

We revisit the common practice of evaluating adaptation of Online Continual Learning (OCL)
algorithms through the metric of online accuracy, which measures the accuracy of the model …

In supervised continual learning, a deep neural network (DNN) is updated with an ever-
growing data stream. Unlike the offline setting where data is shuffled, we cannot make any …

Continual learning (CL) in deep neural networks (DNNs) involves incrementally
accumulating knowledge in a DNN from a growing data stream. A major challenge in CL is …

The rapid development of AI technology in recent years has led to its widespread use in
daily life, where it plays an increasingly important role. In healthcare, AI has been integrated …

Continual Learning (CL) often relies on the availability of extensive annotated datasets, an
assumption that is unrealistically time-consuming and costly in practice. We explore a novel …