Data-driven discovery and parameter estimation of mathematical models in biological pattern formation
Fig 10
Comparison of the prediction errors with MLP model, UMAP, and without dimensionality reduction.
The horizontal axis represents the number of samples used for training. The vertical axis represents the value of the generalization error. Error bars indicate the maximum and minimum values among the five training sessions. The multilayer perceptron (MLP) model trained with contrastive learning (red) was more efficient than both Uniform Manifold Approximation and Projection (UMAP)(green) and the case without dimensionality reduction (blue), even when the sample size was large (indicated by the dashed box).