Table 1.

PCC, expressed in percentage (i.e., PCC $\times$ 100), of the reconstructions in the test set with respect to the ground truths for the approximant (not regularized) and the DNN reconstructions, labeled “DNN,” obtained from the unregularized approximant

		Approximant		DNN		DNN reg.		LT
K	Layer	Simul.	Exp.	Simul.	Exp.	Simul.	Exp.	Simul.	Exp.
1	1	62 $\pm$ 7	48	99 $\pm$ 0.3	80	99 $\pm$ 0.4	72	91 $\pm$ 2	65
1	2	43 $\pm$ 5	22	97 $\pm$ 1	56	96 $\pm$ 1	45	79 $\pm$ 7	37
1	3	49 $\pm$ 9	41	99 $\pm$ 0.4	77	94 $\pm$ 5	76	89 $\pm$ 3	62
1	4	24 $\pm$ 7	7	95 $\pm$ 1	38	92 $\pm$ 2	42	76 $\pm$ 7	27
8	1	75 $\pm$ 63	63	100 $\pm$ 0.2	75	100 $\pm$ 0.1	76	—	—
8	2	57 $\pm$ 6.5	31	98 $\pm$ 0.7	44	99 $\pm$ 0.4	45	—	—
8	3	62 $\pm$ 6.5	52	99 $\pm$ 0.3	80	99 $\pm$ 0.3	79	—	—
8	4	41 $\pm$ 8.1	12	96 $\pm$ 0.8	48	98 $\pm$ 0.6	43	—	—

We show the 2 cases $K=1$ and $K=8$ for the approximant calculation. The LT solution is obtained with $K=30$ and is indicated on the right. The values for the DNN trained with regularized approximants are labeled “DNN reg.” The uncertainty values indicated correspond to the SD over the 50 examples of the test set. For each case, the values for the synthetic (simulated) and experimental examples are indicated in separated columns “Simul.” and “Exp.,” respectively. No uncertainty is given for the experimental case as it contains only 1 example.