One vision of a future artificial intelligence (AI) is where many separate units can learn
independently over a lifetime and share their knowledge with each other. The synergy …

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

Real-time learning concerns the ability of learning systems to acquire knowledge over time,
enabling their adaptation and generalization to novel tasks. It is a critical ability for …

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 …

Multimodal foundation models serve numerous applications at the intersection of vision and
language. Still, despite being pretrained on extensive data, they become outdated over time …

In complex operating environments, rotating equipment may continually generate new fault
categories, affecting the safety of equipment operation, and the number of collected fault …

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 …

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However,
the detection of epileptic seizures remains a significant challenge. Current clinical practice …

Pre-trained deep neural networks (DNNs) are being widely deployed by industry for making
business decisions and to serve users; however, a major problem is model decay, where the …