all AI news
The dynamics of representation learning in shallow, non-linear autoencoders. (arXiv:2201.02115v1 [stat.ML])
Jan. 7, 2022, 2:10 a.m. | Maria Refinetti, Sebastian Goldt
cs.LG updates on arXiv.org arxiv.org
Autoencoders are the simplest neural network for unsupervised learning, and
thus an ideal framework for studying feature learning. While a detailed
understanding of the dynamics of linear autoencoders has recently been
obtained, the study of non-linear autoencoders has been hindered by the
technical difficulty of handling training data with non-trivial correlations -
a fundamental prerequisite for feature extraction. Here, we study the dynamics
of feature learning in non-linear, shallow autoencoders. We derive a set of
asymptotically exact equations that describe …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Senior Manager, IT Ops & Service Management, AI/ML
@ Sephora | San Francisco, CA, US, 50302863
AI/ML Senior Software Engineer (Indonesia)
@ Bjak | Jakarta, Jakarta, Indonesia
Data Engineer
@ Accenture Federal Services | Laurel, MD
Principal Engineer, Deep Learning
@ Outrider | Montreal, Quebec
Consultant Data manager F/H
@ Atos | Bezons, FRANCE, FR, 95870