lowparameterization
Lowparameterization is the design and use of models or representations that describe a system with relatively few parameters or degrees of freedom. The central aim is parsimony: capturing essential structure while limiting flexibility, which can improve interpretability and generalization, particularly when data are scarce, noisy, or expensive to obtain. It contrasts with high-parameterization or overparameterized models that can fit complex patterns but risk overfitting and require larger data sets.
Common approaches include choosing simple parametric families, enforcing structural constraints such as invariances or symmetries, and
The trade-offs include potential underfitting and reduced expressivity if the chosen parameterization is too restrictive. The