So I don't really have a 'deep' understanding of deep learning, but aren't
things like Gaussian RBMs becoming obsolete? I thought I read that Hinton
said that the current state-of-the-art is Really Big networks that just use
standard backprop (plus tricks like dropout). Is that not correct, or is
Hinton's opinion not representative of the current best practices?
On Thu Feb 05 2015 at 9:51:46 AM Kyle Kastner <kastnerk...@gmail.com> wrote:
> I think most of the GP related work is deciding what the sklearn
> compatible interface should be :) specifically how to handle kernels and
> try to share with core codebase.
>
> The HODLR solver of George could be very nice for scalibility but
> algorithm is not easy. There are a few other options on that front but all
> are semi tricky from what I can tell.
>
> Getting GP stuff really nailed will be a good step towards Bayesian
> hyperparameter optimization (or one type) which would be a really killer
> feature if done and integrated well. But a whole lot of work and random
> search is surprisingly good.
>
> W.r.t deep learning what would be added? Gaussian RBM might be nice to
> have.
>
> Kyle
> On Feb 5, 2015 10:40 AM, "Lee Zamparo" <zamp...@gmail.com> wrote:
>
>> With respect to Gaussian processes, there are some good packages in
>> python already (https://github.com/SheffieldML/GPy,
>> https://github.com/dfm/george, probably others). In particular, GPy
>> does not require any other dependencies over and above those already
>> required by sklearn.
>>
>> Maybe a reasonable project would be to wrap a subset of GPy with a
>> sklearn compliant interface? I'm not sure how much work this would
>> be, though.
>>
>> L.
>>
>> On Thu, Feb 5, 2015 at 6:52 AM, Andy <t3k...@gmail.com> wrote:
>> > Hi Christof.
>> > Good question. I don't think we came up with a list yet.
>> > I just looked at the list from last year, and what seems most relevant
>> > still is GMMs,
>> > and possibly the coordinate descent solvers (Alex maybe you can say what
>> > is left there or
>> > if with the SAG we are happy now?)
>> > There is still some deep learning stuff that we might want to include,
>> > but we need to merge
>> > the MLP first.
>> > I think it would also be interesting to rework the Gaussian processes,
>> > but that might be a bit to ambitious for a GSOC project.
>> >
>> > If anyone has any other ideas, maybe list them in this thread. Also,
>> > possible mentors, please speak up :)
>> >
>> > Cheers,
>> > Andreas
>> >
>> > On 02/04/2015 11:14 PM, Christof Angermueller wrote:
>> >> Hi all,
>> >>
>> >> is there already a list of potential Google Summer of Code (GSoC) 2015
>> >> projects?
>> >> Knowing about potential projects would allow me start working on
>> certain
>> >> ideas early.
>> >>
>> >> Cheers,
>> >> Christof
>> >>
>> >
>> >
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