Approaches to continual learning aim to successfully learn a set of related tasks that arrive in
an online manner. Recently, several frameworks have been developed which enable deep …

Stochastic gradient descent requires that training samples be drawn from a uniformly
random distribution of the data. For a deployed system that must learn online from an …

The human brain is the gold standard of adaptive learning. It not only can learn and benefit
from experience, but also can adapt to new situations. In contrast, deep neural networks only …

Continual learning (CL) is a paradigm which addresses the issue of how to learn from
sequentially arriving tasks. The goal of this paper is to introduce a CL framework which can …

Abstract Machine learning techniques have found extensive application in dynamic fields
like drones, self-driving vehicles, surveillance, and more. Their effectiveness stems from …

Abstract Computing with Artificial Neural Networks (ANNs) is a branch of machine learning
that has seen substantial growth over the last decade, significantly increasing the accuracy …

The rapid advancement of Deep Neural Networks (DNNs), computing, and sensing
technology has enabled many new applications, such as the self-driving vehicle, the …