Repeated features in Neural Networks with tabular data
Strictly theoretically it makes no difference on accuracy.
Here is why: We already know mathematically that NN can approximate any function. So lets say that we have Input X. X is highly correlated, than we can apply a decorrelation technique out there. Main Thing is, you get X` that has different numerical representation. Most likely more difficult for NN to learn to map to Outputs y. But still in Theory you can Change the architecure, Train for longer and you can still get the same Approximation, i.e. Accuracy.
Now, Theory and Praxis are same in Theory but different in Praxis, and I suspect that this Adjustments of Architecture etc will be much more costly in reality depending on the dataset.