First, to quantify the quality of a single NN 1k approximation to the LS method, the MSE loss [Eq. ] between the LS method cut locations and the NN 1k cut locations for the validation set are computed for each value of 𝑁 , 0 ≤ 𝑁 ≤ 31 (gray), see Fig. of this loss (black) over all LS descendants, 𝑁 , is shown as a function of 𝑏. It is immediately evident from the small MSE loss (∼ 10 -10 ) that, despite the variability from one 𝑁 value to another, the NN 1k network is robust in interpolating the LS method cut locations for all 𝑏. The NN 1k network is trained on 1 000 evenly spaced values for 𝑏 but the validation dataset used for the data in Fig. is based on 10 000 values for 𝑏. Thus, this NN 1k not only accurately replicates the training values but also interpolates between training values.