diff --git a/content/02.main-text.md b/content/02.main-text.md
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@@ -115,6 +115,10 @@ Our models were trained for 100 epochs of mini-batch stochastic gradient descent
 To select the remaining hyperparameters for each hidden layer size, we performed a random search over 10 combinations, with a single train/test split stratified by cancer type, using the following hyperparameter ranges: learning rate {0.1, 0.01, 0.001, 5e-4, 1e-4}, dropout proportion {0.1, 0.5, 0.75}, weight decay (L2 penalty) {0, 0.1, 1, 10, 100}.
 We used the same train/cross-validation split strategy described above for one random seed and 4 cross-validation splits, generating 4 different performance measurements for each gene and hidden layer size.
 
+Although L1 regularization can be used to more directly induce model sparsity in convex settings, we note that using L1 regularization to control model complexity in neural networks is considerably more complex.
+Simply adding an additional loss term is not enough to achieve convergence to a sparse solution; the problem requires special optimizers and is the subject of ongoing research (see, e.g., [@url:https://dl.acm.org/doi/abs/10.5555/3540261.3542126]).
+For this reason, we focused on controlling NN model complexity via the size and number of hidden layers, as well as the other approaches described above.
+
 For the _EGFR_ gene, we also ran experiments where we varied the dropout proportion and the weight decay hyperparameter as the regularization axis, and selected the remaining hyperparameters (including the hidden layer size) using a random search.
 In these cases, we used a fixed range for dropout of {0.0, 0.05, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 0.95}, and a fixed range for weight decay of {0.0, 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.75, 1.0, 10.0}.
 All neural network analyses were performed on a Ubuntu 18.04 machine with a NVIDIA RTX 2060 GPU.