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 incorporating spatial information to tackle the opaque parameterization process of random weighted neural networks. The INN leverages spatial information to elucidate the connection between parameters and network residuals. Furthermore%2C it devises a geometric relationship strategy using a pool of candidate nodes and established relationships to select node parameters conducive to network convergence. Additionally%2C a lightweight version of INN tailored for large-scale data modeling tasks is proposed. The paper also showcases the infinite approximation property of INN. Experimental findings on various benchmark datasets and real-world industrial cases demonstrate INN's superiority over other neural networks of the same type in terms of modeling speed%2C accuracy%2C and network structure.?quality=80&w=800)
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- Interpretable Neural Networks with Random Constructive Algorithm | AI Research Paper Details
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- Interpretable Neural Networks with Random Constructive Algorithm | AI Research Paper Details
- Interpretable Neural Networks with Random Constructive Algorithm | AI Research Paper Details
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- Figure 1 from Interpretable Neural Networks with Random Constructive Algorithm | Semantic Scholar
- Figure 1 from Interpretable Neural Networks with Random Constructive Algorithm | Semantic Scholar
- Figure 5 from Interpretable Neural Networks with Random Constructive Algorithm | Semantic Scholar
- Figure 2 from Interpretable Neural Networks with Random Constructive Algorithm | Semantic Scholar