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What is Unveiling Neural Network Semantics with SRNet about?
The paper presents SRNet, a novel symbolic regression method designed to uncover hidden semantics in neural networks (NNs) by generating explicit mathematical representations of their layers. SRNet employs a multi-chromosome Cartesian genetic programming approach to simultaneously explain the semantics of all layers, overcoming limitations of existing local and global explanation methods. Experimental results demonstrate that SRNet achieves higher interpolation accuracy compared to traditional methods like
- Author
- rodrigo Santos Batista
- Language
- EN