Feedforward Neural Networks with Diffused Nonlinear Weight Functions
<|reference_start|>Feedforward Neural Networks with Diffused Nonlinear Weight Functions: In this paper, feedforward neural networks are presented that have nonlinear weight functions based on look--up tables, that are specially smoothed in a regularization called the diffusion. The idea of such a type of networks is based on the hypothesis that the greater number of adaptive parameters per a weight function might reduce the total number of the weight functions needed to solve a given problem. Then, if the computational complexity of a propagation through a single such a weight function would be kept low, then the introduced neural networks might possibly be relatively fast.   A number of tests is performed, showing that the presented neural networks may indeed perform better in some cases than the classic neural networks and a number of other learning machines.<|reference_end|>
@article{rataj2003feedforward,
    title={Feedforward Neural Networks with Diffused Nonlinear Weight Functions},
    author={Artur Rataj},
    journal={arXiv preprint arXiv:cs/0310050},
    year={2003},
    number={IITiS-2003-02-23-1-1.06},
    archivePrefix={arXiv},
    eprint={cs/0310050},
    primaryClass={cs.NE}
}