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- W4294635596 abstract "We describe a novel family of models of multi- layer feedforward neural networks in which the activation functions are encoded via penalties in the training problem. Our approach is based on representing a non-decreasing activation function as the argmin of an appropriate convex optimiza- tion problem. The new framework allows for algo- rithms such as block-coordinate descent methods to be applied, in which each step is composed of a simple (no hidden layer) supervised learning problem that is parallelizable across data points and/or layers. Experiments indicate that the pro- posed models provide excellent initial guesses for weights for standard neural networks. In addi- tion, the model provides avenues for interesting extensions, such as robustness against noisy in- puts and optimizing over parameters in activation functions." @default.
- W4294635596 created "2022-09-05" @default.
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- W4294635596 date "2018-05-03" @default.
- W4294635596 modified "2023-09-30" @default.
- W4294635596 title "Lifted Neural Networks" @default.
- W4294635596 doi "https://doi.org/10.48550/arxiv.1805.01532" @default.
- W4294635596 hasPublicationYear "2018" @default.
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