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- W2617086554 abstract "Neural networks have been shown to have a remarkable ability to uncover low dimensional structure in data: the space of possible reconstructed images form a reduced model manifold in image space. We explore this idea directly by analyzing the manifold learned by Deep Belief Networks and Stacked Denoising Autoencoders using Monte Carlo sampling. The model manifold forms an only slightly elongated hyperball with actual reconstructed data appearing predominantly on the boundaries of the manifold. In connection with the results we present, we discuss problems of sampling high-dimensional manifolds as well as recent work [M. Transtrum, G. Hart, and P. Qiu, Submitted (2014)] discussing the relation between high dimensional geometry and model reduction." @default.
- W2617086554 created "2017-06-05" @default.
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- W2617086554 date "2017-05-25" @default.
- W2617086554 modified "2023-09-27" @default.
- W2617086554 title "Jeffrey's prior sampling of deep sigmoidal networks" @default.
- W2617086554 hasPublicationYear "2017" @default.
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