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 …

Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective change through time, or where all the training data and …

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 …

Abstract According to Complementary Learning Systems (CLS) theory~\cite
{mcclelland1995there} in neuroscience, humans do effective\emph {continual learning} …

Continual learning (CL) learns a sequence of tasks incrementally. There are two popular CL
settings, class incremental learning (CIL) and task incremental learning (TIL). A major …

Deep learning has improved performance on many natural language processing (NLP)
tasks individually. However, general NLP models cannot emerge within a paradigm that …

This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing
ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the …

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 …

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine
learning paradigm that continuously learns by accumulating past knowledge that it then …

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 …