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- W3208264711 abstract "Convolutional Neural Networks (CNNs) have been widely applied. But as the CNNs grow, the number of arithmetic operations and memory footprint also increase. Furthermore, typical non-linear activation functions do not allow associativity of the operations encoded by consecutive layers, preventing the simplification of intermediate steps by combining them. We present a new activation function that allows associativity between sequential layers of CNNs. Even though our activation function is non-linear, it can be represented by a sequence of linear operations in the conformal model for Euclidean geometry. In this domain, operations like, but not limited to, convolution, average pooling, and dropout remain linear. We take advantage of associativity to combine all the “conformal layers” and make the cost of inference constant regardless of the depth of the network." @default.
- W3208264711 created "2021-11-08" @default.
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- W3208264711 date "2021-10-01" @default.
- W3208264711 modified "2023-09-26" @default.
- W3208264711 title "ConformalLayers: A non-linear sequential neural network with associative layers" @default.
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- W3208264711 doi "https://doi.org/10.1109/sibgrapi54419.2021.00059" @default.
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