I have the same feeling. On Thu, Feb 05, 2015 at 03:56:12PM +0000, Thomas Johnson wrote: > 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 > > ------------------------------------------------------------------------------ > > Dive into the World of Parallel Programming. 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