Santa Fe Institute's video: Swimming Without a Lifeguard in the Ocean of Ideas: Abstraction Unsupervised Deep Learning

Kiel Howe, Minerva Schools at KGI Deep learning models are known to effectively discover useful abstractions (latent features) of input data in their hidden layers. Unsupervised deep learning techniques have been used to systematically discover these latent features in unlabeled data, and progress has been made by several groups in methods for discovering hierarchical latent feature spaces. Such hierarchical latent feature spaces can improve interpretability and performance in downstream supervised learning tasks and play an important role in deep generative models. I will give an overview of some of these existing techniques for finding hierarchical latent feature spaces and present my recent work on another approach. I'll also briefly speculate about how such representations may play a role in building a bridge between knowledge representations in neural and symbolic learning systems. The talk will be given dubbed from the original language of machine learning into English, with subtitles provided for native speakers of statistical physics, information theory, and tensor networks / graphical models. Learn more at https://santafe.edu Follow us on social media: https://twitter.com/sfiscience https://instagram.com/sfiscience https://facebook.com/santafeinstitute https://facebook.com/groups/santafeinstitute https://linkedin.com/company/santafeinstitute