Nonlinear ICA using temporal structure: a principled framework for unsupervised deep learning
Microsoft
Nonlinear ICA using temporal structure: a principled framework for unsupervised deep learning
Unsupervised learning, in particular learning general nonlinear representations, is one of the deepest problems in machine learning. Estimating latent quantities in a generative model provides a principled framework, and has been successfully used in the linear case, e.g. with independent component analysis (ICA) and sparse coding. However ...
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