Incrementally learning new information from a non-stationary stream of data, referred to as
'continual learning', is a key feature of natural intelligence, but a challenging problem for …

Abstract The field of Continual Learning investigates the ability to learn consecutive tasks
without losing performance on those previously learned. The efforts of researchers have …

In the online continual learning paradigm, agents must learn from a changing distribution
while respecting memory and compute constraints. Experience Replay (ER), where a small …

Continual learning (CL) is a setting in which a model learns from a stream of incoming data
while avoiding to forget previously learned knowledge. Pre-trained language models (PLMs) …

Online continual learning aims to get closer to a live learning experience by learning directly
on a stream of data with temporally shifting distribution and by storing a minimum amount of …

Progress in continual reinforcement learning has been limited due to several barriers to
entry: missing code, high compute requirements, and a lack of suitable benchmarks. In this …

Continual learning is the problem of learning from a nonstationary stream of data, a
fundamental issue for sustainable and efficient training of deep neural networks over time …

What is the state of the art in continual machine learning? Although a natural question for
predominant static benchmarks, the notion to train systems in a lifelong manner entails a …

The advancement of continual reinforcement learning (RL) has been facing various
obstacles, including standardized metrics and evaluation protocols, demanding …

Continual Learning (CL, sometimes also termed incremental learning) is a flavor of machine
learning where the usual assumption of stationary data distribution is relaxed or omitted …