Re:Neural Network Loss Landscapes: What do we know? (2021)
by sdenton4 on Sunday July 17, 2022 @10:05PM
(#32133741)
The "functional equivalence" discussed in the past gets at some of this. There's definitely more going on than permutation symmetry; in particular, if two solutions are symmetrically related (and thus evaluating the same overall function) then ensembling them together shouldn't help. But ensembling /does/ still help in many cases.
The different solutions found in different runs likely share a lot of information, but learn some different things on the edges. It would be cool to isolate the difference between two networks...
The different solutions found in different runs likely share a lot of information, but learn some different things on the edges. It would be cool to isolate the difference between two networks...