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 low amount of publications.
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 any issues given the data.
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 low amount of publications.
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 any issues given the data.