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- W4301023013 abstract "We propose a novel activation function that implements piece-wise orthogonal non-linear mappings based on permutations. It is straightforward to implement, and very computationally efficient, also it has little memory requirements. We tested it on two toy problems for feedforward and recurrent networks, it shows similar performance to tanh and ReLU. OPLU activation function ensures norm preservance of the backpropagated gradients, therefore it is potentially good for the training of deep, extra deep, and recurrent neural networks." @default.
- W4301023013 created "2022-10-04" @default.
- W4301023013 creator A5006823112 @default.
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- W4301023013 date "2016-04-08" @default.
- W4301023013 modified "2023-09-25" @default.
- W4301023013 title "Norm-preserving Orthogonal Permutation Linear Unit Activation Functions (OPLU)" @default.
- W4301023013 doi "https://doi.org/10.48550/arxiv.1604.02313" @default.
- W4301023013 hasPublicationYear "2016" @default.
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