Machine learning has achieved remarkable success in many applications. However,
existing studies are largely based on the closed-world assumption, which assumes that the …

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 …

Open-world learning is a problem where an autonomous agent detects things that it does
not know and learns them over time from a non-stationary and never-ending stream of data; …

Although deep learning has made significant progress on fixed large-scale datasets, it
typically encounters challenges regarding improperly detecting unknown/unseen classes in …

This paper identifies the flaws in existing open-world learning approaches and attempts to
provide a complete picture in the form of\textbf {True Open-World Learning}. We accomplish …

Learn to solve challenging data science problems by building powerful machine learning
models using Python About This Book Understand which algorithms to use in a given …

As science attempts to close the gap between man and machine by building systems
capable of learning, we must embrace the importance of the unknown. The ability to …

At present, object recognition studies are mostly conducted in a closed lab setting with
classes in test phase typically in training phase. However, real-world problem are far more …

Open-world machine learning (ML) combines closed-world models trained on in-distribution
data with out-of-distribution (OOD) detectors, which aim to detect and reject OOD inputs …

As more and more AI agents are used in practice, it is time to think about how to make these
agents fully autonomous so that they can learn by themselves in a self-motivated and self …