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- W2909957174 abstract "Recent work has noted that all bad local minima can be removed from neural network loss landscapes, by adding a single unit with a particular parameterization. We show that the core technique from these papers can be used to remove all bad local minima from any loss landscape, so long as the global minimum has a loss of zero. This procedure does not require the addition of auxiliary units, or even that the loss be associated with a neural network. The method of action involves all bad local minima being converted into bad (non-local) minima at infinity in terms of auxiliary parameters." @default.
- W2909957174 created "2019-01-25" @default.
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- W2909957174 date "2019-01-12" @default.
- W2909957174 modified "2023-09-25" @default.
- W2909957174 title "Eliminating all bad Local Minima from Loss Landscapes without even adding an Extra Unit" @default.
- W2909957174 cites W2908206262 @default.
- W2909957174 doi "https://doi.org/10.48550/arxiv.1901.03909" @default.
- W2909957174 hasPublicationYear "2019" @default.
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