Biological organisms learn from interactions with their environment throughout their lifetime.
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …

Humans and animals have the ability to continually acquire, fine-tune, and transfer
knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is …

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 …

Deep neural networks are used in many state-of-the-art systems for machine perception.
Once a network is trained to do a specific task, eg, bird classification, it cannot easily be …

Replay is the reactivation of one or more neural patterns that are similar to the activation
patterns experienced during past waking experiences. Replay was first observed in …

Artificial neural networks thrive in solving the classification problem for a particular rigid task,
where the network resembles a static entity of knowledge, acquired through generalized …

Current deep learning methods are regarded as favorable if they empirically perform well on
dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual …

Modularity is a compelling solution to continual learning (CL), the problem of modeling
sequences of related tasks. Learning and then composing modules to solve different tasks …

Learning continuously during all model lifetime is fundamental to deploy machine learning
solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with …

The stability-plasticity dilemma is a wellknow constraint for artificial and biological neural
systems. The basic idea is that learning in a parallel and distributed system requires …