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Researcher here (in ML), our field is so full of noisy results due to this issue. Everyone talks about it, but you can't get around the fact that you no longer can get away with a …

The issue is it takes away a significant amount of utility without adding any. Volume/brightness is slightly better, but you can get the same effect by holding down the physical bu…

Based off of the results, you have to train a larger number of architectures to identify the right subnetwork.

There are approaches to ensure parameters remain stable despite the depth (selu, for example).

Nothing wrong with an SVM. How else would they create a decision boundary for classifying patients? The choice of the polynomial kernel is interesting, but I don't think it causes …

Spot on. Just a heads up, there is a decent amount of work on using convolutions to condense the initial representations and can reduce computation time equal to your max pooling. …