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- W4313531275 abstract "Computational units in artificial neural networks compute a linear combination of their inputs, and then apply a nonlinear filter, often a ReLU shifted by some bias, and if the inputs come themselves from other units, they were already filtered with their own biases. In a layer, multiple units share the same inputs, and each input was filtered with a unique bias, resulting in output values being based on shared input biases rather than individual optimal ones. To mitigate this issue, we introduce DAC, a new computational unit based on preactivation and multiple biases, where input signals undergo independent nonlinear filtering before the linear combination. We provide a Keras implementation and report its computational efficiency. We test DAC convolutions in ResNet architectures on CIFAR-10, CIFAR-100, Imagenette, and Imagewoof, and achieve performance improvements of up to 1.73%. We exhibit examples where DAC is more efficient than its standard counterpart as a function approximator, and we prove a universal representation theorem." @default.
- W4313531275 created "2023-01-06" @default.
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- W4313531275 date "2023-01-02" @default.
- W4313531275 modified "2023-09-23" @default.
- W4313531275 title "Improving Performance in Neural Networks by Dendrites-Activated Connections" @default.
- W4313531275 doi "https://doi.org/10.48550/arxiv.2301.00924" @default.
- W4313531275 hasPublicationYear "2023" @default.
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