Bayesian Nonparametrics for Non-exhaustive Learning

no code implementations • 26 Aug 2019 • Yicheng Cheng, Bartek Rajwa, Murat Dundar

Non-exhaustive learning (NEL) is an emerging machine-learning paradigm designed to confront the challenge of non-stationary environments characterized by anon-exhaustive training sets lacking full information about the available classes. Unlike traditional supervised learning that relies on fixed models, NEL utilizes self-adjusting machine learning to better accommodate the non-stationary nature of the real-world problem, which is at the root of many recently discovered limitations of deep learning.

BIG-bench Machine Learning open-set classification +1