In my opinion, Adagrad is still on the lower side in terms of number of
citations (currently 107 according to Google) for inclusion into
scikit-learn. So unless there is strong evidence that it outperforms other
solvers (e.g., in computer vision or NLP papers that use Adagrad), I'm
personally -1 for its inclusion into scikit-learn.
Mathieu
On Sat, Dec 21, 2013 at 5:24 AM, Manoj Kumar <[email protected]
> wrote:
> Hello,
>
> I would be an applicant to GSoC 2014 under Scikit-Learn (as an org /
> suborg under PSF). I've worked on a number of issues,
> 2598<https://github.com/scikit-learn/scikit-learn/pull/2598>,
> 2493 <https://github.com/scikit-learn/scikit-learn/pull/2493> (unmerged)
> and 2590 <https://github.com/scikit-learn/scikit-learn/pull/2590> ,
> 2566<https://github.com/scikit-learn/scikit-learn/pull/2566>
> <https://github.com/scikit-learn/scikit-learn/pull/2598>
> (merged), so I believe I've satisfied the patch requirements. Since I
> guess this is a bit early (please correct me if I'm wrong) to zero in on an
> idea, and start working on a proposal, I thought I might try solving some
> more issues.
>
> Since I am familiar with Stochastic Gradient Descent, I thought I might
> try working on this issue,
> https://github.com/scikit-learn/scikit-learn/issues/1209 . I went through
> the research paper, however some parts of it seem to be a bit more
> advanced. It would be really helpful if someone could give me a rough
> idea/gist on what has to be implemented and how it fits with the existing
> SGD.
>
> Thanks
> --
> Regards,
> Manoj Kumar,
> Mech Undergrad
> http://manojbits.wordpress.com
>
>
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