Neural Network Models for Contextual Regression
We propose a neural network model for contextual regression in which the regression model depends on contextual features that determine the active submodel and an algorithm to fit the model. The proposed simple contextual neural network (SCtxtNN) separates context identification from context-specific regression, resulting in a structured and interpretable architecture with fewer parameters than a fully connected feed-forward network. We show mathematically that the proposed architecture is sufficient to represent contextual linear regression models using only standard neural network components. Numerical experiments are provided to support the theoretical result, showing that the proposed...
| Authors | Seksan Kiatsupaibul, Pakawan Chansiripas |
|---|---|
| Primary category | stat.ML |
| Published | 25 March 2026 |
| Last updated at source | 19 May 2026 |
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