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

Machine learning (ML) has been instrumental for the advances of both data analysis and
artificial intelligence (AI). The recent success of deep learning brings it to a new height. ML …

Abstract Lifelong Machine Learning, or LML, considers systems that can learn many tasks
from one or more domains over its lifetime. The goal is to sequentially retain learned …

Given the recent success of Deep Learning applied to a variety of single tasks, it is natural to
consider more human-realistic settings. Perhaps the most difficult of these settings is that of …

Abstract Machine learning has not yet succeeded in the design of robust learning algorithms
that generalize well from very small datasets. In contrast, humans often generalize correctly …

We propose a novel deep network architecture for lifelong learning which we refer to as
Dynamically Expandable Network (DEN), that can dynamically decide its network capacity …

Lifelong learning aims at adapting a learned model to new tasks while retaining the
knowledge gained earlier. A key challenge for lifelong learning is how to strike a balance …

In a lifelong learning framework, an agent acquires knowledge incrementally over
consecutive learning tasks, continually building upon its experience. Recent lifelong …

Time-dependent data-generating distributions have proven to be difficult for gradient-based
training of neural networks, as the greedy updates result in catastrophic forgetting of …

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 …