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- W1572325259 abstract "We use sampling methods to analyse the “apparent minima” of the error surfaces of feedforward neural networks learning encoder problems. First and second-order statistics of a sample of these points of attraction are shown to provide qualitative statistical information about the structure of the error surface, allowing a simple description of this structure. Following methods previously used in the analysis of other complex configuration spaces (such as spin glass models and several combinatorial optimization problems), the third-order statistics of the points of attraction are examined and found to be arranged in a highly ultrametric way, using the normal Euclidean distance measure. The implications of this result are discussed." @default.
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- W1572325259 date "1998-01-01" @default.
- W1572325259 modified "2023-09-26" @default.
- W1572325259 title "Ultrametric Structure in Autoencoder Error Surfaces" @default.
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- W1572325259 doi "https://doi.org/10.1007/978-1-4471-1599-1_23" @default.
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