I’m not in these fields, so take everything I say very lightly, but intuitively this feels wrong to me. I understood your point to be something like: the labels are doing all the work. But for me, the labels are not what makes those approaches seem more interpretable than a DNN. It’s that in a DNN, the features are not automatically locatable (even pseudonymously so) in a way that lets you figure out the structure /shape that separates them—each training run of the model is learning a new way to separate them and it isn’t clear how to know what those shapes tend to turn out as and why. However, the logic graphs already agree with you an initial structure/shape.
Of course there are challenges in scaling up the other methods, but I think claiming they’re no more interpretable than DNNs feels incorrect to me. [Reminder, complete outsider to these fields].
Even if you could find some notion of a, b, c we think are features in this DNN—how would you know you were right? How would you know you’re on the correct level of abstraction / cognitive separation / carving at the joints instead of right through the spleen and then declaring you’ve found a, b and c. It seems this is much harder than in a model where you literally assume the structure and features all upfront.
I’m not in these fields, so take everything I say very lightly, but intuitively this feels wrong to me. I understood your point to be something like: the labels are doing all the work. But for me, the labels are not what makes those approaches seem more interpretable than a DNN. It’s that in a DNN, the features are not automatically locatable (even pseudonymously so) in a way that lets you figure out the structure /shape that separates them—each training run of the model is learning a new way to separate them and it isn’t clear how to know what those shapes tend to turn out as and why. However, the logic graphs already agree with you an initial structure/shape.
Of course there are challenges in scaling up the other methods, but I think claiming they’re no more interpretable than DNNs feels incorrect to me. [Reminder, complete outsider to these fields].
Even if you could find some notion of a, b, c we think are features in this DNN—how would you know you were right? How would you know you’re on the correct level of abstraction / cognitive separation / carving at the joints instead of right through the spleen and then declaring you’ve found a, b and c. It seems this is much harder than in a model where you literally assume the structure and features all upfront.