The simplest model of unsupervised feature learning

量子物理学・ナノサイエンス第171回セミナー

概要

Learning hidden features in unlabeled training data is called unsupervised learning. Understanding how data size confines learning process is a topic of interest not only in machine learning but also in cognitive neuroscience. The merit of unsupervised feature learning puzzles the community for a long time, and now as deep learning gets popular and powerful, a theoretical basis for unsupervised learning becomes increasingly important but is lacked so far. Our simple statistical mechanics model substantially advances our understanding of how data size confines learning, and opens a new perspective for both neural network training and related statistical physics studies.

• Unsupervised feature learning from finite data by message passing:discontinuous versus continuous phase transition | Cornell University Library
• 量子物理学・ナノサイエンス第171回セミナー

• ナノサイエンス・量子物理学国際研究センター 主催
• 東京工業大学理学院・物理学系、「ナノサイエンスを拓く量子物理学拠点」共催