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- W3204351434 abstract "In this paper, we introduce a novel non-linear activation function that spontaneously induces class-compactness and regularization in the embedding space of neural networks. The function is dubbed DOME for Difference Of Mirrored Exponential terms. The basic form of the function can replace the sigmoid or the hyperbolic tangent functions as an output activation function for binary classification problems. The function can also be extended to the case of multi-class classification, and used as an alternative to the standard softmax function. It can also be further generalized to take more flexible shapes suitable for intermediate layers of a network. In this version of the paper, we only introduce the concept. In a subsequent version, experimental evaluation will be added." @default.
- W3204351434 created "2021-10-11" @default.
- W3204351434 creator A5028776484 @default.
- W3204351434 creator A5078745193 @default.
- W3204351434 date "2021-09-30" @default.
- W3204351434 modified "2023-09-27" @default.
- W3204351434 title "Introducing the DOME Activation Functions." @default.
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