2010
AISTATS
AISTATS 2010
Parametric Herding
Abstract
A parametric version of herding is formulated. The nonlinear mapping between consecutive time slices is learned by a form of self-supervised training. The resulting dynamical system generates pseudo-samples that resemble the original data. We show how this parametric herding can be successfully used to compress a dataset consisting of binary digits. It is also verified that high compression rates translate into good prediction performance on unseen test data.
🚀
Conference Pioneer
— AISTATS 2010
🧭
Keyword Pioneer
— self-supervised training
🐣
Hot Topic Early Bird
— dynamical system
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio