To whoever programmed the MLPClassifier (with the L-BFGS solver),
I just wanted to personally thank you and if I get your name(s), I would
mention it/them in my paper additionally to the mandatory sklearn citation.
I hope that sklearn will be keeping this algorithm forever in their library
despite the increasing amount of established deep learning libraries that seem
to make this code obsolete. For my small scale, more theoretic analysis, it
worked much better than any other algorithm and I would not have gotten such
surprising results. Due to the high quality implementation, the integration of
a much better solver than SGD, and the respective good documentation, I could
show empirically how the VC dimension and another property of MLPs (MacKay
dimension) actually scale linear with the number of edges in the respective
graph which helped us to provide a new much more strict upper bound
(https://arxiv.org/abs/1708.06019 <https://arxiv.org/abs/1708.06019>). This
would have not been possible with other implementations. If there is an
interest by the developers, I could try to contribute a tutorial documentation
for sklearn. Just let me know.
Thank you a lot!!!
Best,
Mario
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