Biological systems, including human beings, have the innate ability to perform complex
tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to …

Traditional machine learning, mainly supervised learning, follows the assumptions of closed-
world learning, ie, for each testing class, a training class is available. However, such …

Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …

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

Traditional supervised learning makes the closed-world assumption that the classes
appeared in the test data must have appeared in training. This also applies to text learning …

Current deep learning methods are regarded as favorable if they empirically perform well on
dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual …

The arrival of the intelligent manufacturing and industrial internet era brings more and more
opportunities and challenges to modern industry. Specifically, the revolution of the …

Classic supervised learning makes the closed-world assumption that the classes seen in
testing must have appeared in training. However, this assumption is often violated in real …

Lifelong learning is the problem of learning multiple consecutive tasks in a sequential
manner, where knowledge gained from previous tasks is retained and used to aid future …

Existing neural network based one-class learning methods mainly use various forms of auto-
encoders or GAN style adversarial training to learn a latent representation of the given one …