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 …

Recent breakthroughs in neurobiology indicate that the time is ripe to understand how
cellular-level mechanisms are related to conscious experience. Here, we highlight the …

The ability to learn new concepts continually is necessary in this ever-changing world.
However, deep neural networks suffer from catastrophic forgetting when learning new …

We address the problem of class incremental learning, which is a core step towards
achieving adaptive vision intelligence. In particular, we consider the task setting of …

Neuroscience has focused on the detailed implementation of computation, studying neural
codes, dynamics and circuits. In machine learning, however, artificial neural networks tend …

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 …

Continual learning methods aim at training a neural network from sequential data with
streaming labels, relieving catastrophic forgetting. However, existing methods are based on …

Cybernetics, the study of goal-directed, adaptive systems, is the best framework for an
integrative theory of personality. Cybernetic Big Five Theory attempts to provide a …

Memory is fleeting. New material rapidly obliterates previous material. How, then, can the
brain deal successfully with the continual deluge of linguistic input? We argue that, to deal …

Mixed data comprises both numeric and categorical features, and mixed datasets occur
frequently in many domains, such as health, finance, and marketing. Clustering is often …