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 …

Wearable devices are a fast-growing technology with impact on personal healthcare for both
society and economy. Due to the widespread of sensors in pervasive and distributed …

Bayesian formulations of deep learning have been shown to have compelling theoretical
properties and offer practical functional benefits, such as improved predictive uncertainty …

Artificial neural networks thrive in solving the classification problem for a particular rigid task,
acquiring knowledge through generalized learning behaviour from a distinct training phase …

Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten
by new incoming information while important, frequently used knowledge is prevented from …

We introduce a conceptually simple and scalable framework for continual learning domains
where tasks are learned sequentially. Our method is constant in the number of parameters …

Artificial neural networks suffer from catastrophic forgetting when they are sequentially
trained on multiple tasks. To overcome this problem, we present a novel approach based on …

This paper develops variational continual learning (VCL), a simple but general framework
for continual learning that fuses online variational inference (VI) and recent advances in …

Continual Learning (CL) research typically focuses on tackling the phenomenon of
catastrophic forgetting in neural networks. Catastrophic forgetting is associated with an …

It was recently shown that architectural, regularization and rehearsal strategies can be used
to train deep models sequentially on a number of disjoint tasks without forgetting previously …