Something that could go by the same title is the use of tensor networks for ML. I think it works like a pre-optimization step by dimension reduction of the solution space, but if someone could give the right intuitive explanation I'd be much obliged.
It seems to be a way to lessen inductive bias by making decisions about available ML algos. That is, it vastly increases the solution space but remains effective by omitting unlikely solutions.
It seems to be a way to lessen inductive bias by making decisions about available ML algos. That is, it vastly increases the solution space but remains effective by omitting unlikely solutions.