Hello Chenzhe, > I guess I need to make up a whole plan and write it into the proposal?
A good plan forms the basis for a good and comprehensive proposal. There are a lot of useful tips out there like https://developers.google.com/open- <https://developers.google.com/open-> source/gsoc/resources/manual may be helpful. Thanks, Marcus > On 16 Mar 2017, at 19:37, Chenzhe Diao <williamd...@gmail.com> wrote: > > Thanks Ryan! I will read the code in more details and think of it. I guess I > need to make up a whole plan and write it into the proposal? > > Great to know you guys Shangtong and Bang. Since you both have experiences in > this GSOC, I can learn a lot from you. > > Best, > Chenzhe > > On Thu, Mar 16, 2017 at 9:02 AM, Shangtong Zhang > <zhangshangtong....@gmail.com <mailto:zhangshangtong....@gmail.com>> wrote: > Haha, maybe we don’t know each other. We are from 3 different departments, > CS, ECE and MATH. > But I think it’s a good chance to know each other. > > Shangtong Zhang, > First year graduate student, > Department of Computing Science, > University of Alberta > Github <https://github.com/ShangtongZhang> | Stackoverflow > <http://stackoverflow.com/users/3650053/slardar-zhang> >> On Mar 16, 2017, at 08:11, Ryan Curtin <r...@ratml.org >> <mailto:r...@ratml.org>> wrote: >> >> On Wed, Mar 15, 2017 at 05:33:15AM -0600, Chenzhe Diao wrote: >>> Hello everyone, >>> >>> My name is Chenzhe. I am a 4th year Ph.D. student in Applied Mathematics >>> from University of Alberta in Canada. Part of my research is about image >>> recovery using over-complete systems (wavelet frames), which involves some >>> machine learning techniques, and uses sparse optimization techniques as one >>> of the key steps. So I am quite interested in the project about "Low >>> rank/sparse optimization using Frank-Wolfe". >>> >>> I checked the mailing list from last year. It seems that there was one >>> student from GSOC16 interested in a similar project. Is that still not done >>> for some special difficulties? I took a brief look of the Martin Jaggi >>> paper, >>> it seems that the algorithm is not complicated by itself. So I guess most >>> of the time for the project would be to implement the algorithm in desired >>> form, and to make extensive tests? What kinds of tests are we expecting? >>> >>> Also, I checked src/mlpack/core/optimizers/ and I saw the GradientDescent >>> class implemented. I guess I need to write a new class in similar structure? >> >> Hi Chenzhe, >> >> Do you know Shangtong Zhang? He is a first-year MSc student who also >> attends the University of Alberta. Or Bang Liu? He also is a PhD >> student at UofA and was a part of mlpack GSoC last year. Maybe you guys >> all know each other? It seems like it's a big university though (nearly >> 40k students) so maybe the chances are small. :) >> >> Nobody implemented the Frank-Wolfe optimizer from last year, so the >> project (and related projects) are still open. Anything you find in >> src/mlpack/core/optimizers/ is what we have, although there are a few >> open PRs related to this issue: >> >> https://github.com/mlpack/mlpack/issues/893 >> <https://github.com/mlpack/mlpack/issues/893> >> >> But those are not F-W, those are basically other optimizers related to >> SGD. >> >> Essentially you are right, the idea of the project would be to provide >> an implementation of the algorithm in Jaggi's paper. In your case given >> your background and expertise, this will probably be a relatively >> straightforward task. Testing the algorithm has some difficulty but >> honestly I suspect it can be tested mostly like the other optimizers: >> come up with some easy and hard problems to optimize, and make sure that >> the implemented F-W algorithm can successfully find the minimum. You >> can take a look at the existing tests for other optimizers in >> src/mlpack/tests/ to get some kind of an idea for how to do that. >> >> Building on top of that, there are many further places you could go with >> the project: >> >> * you could modify the various mlpack programs like >> mlpack_logistic_regression and mlpack_softmax_regression and so >> forth to expand the list of available optimizers >> >> * you could benchmark the F-W optimizer against other optimizers on >> various problems and possibly (depending on the results) assemble >> something that could be published >> >> * you could try implementing some new ideas based on the stock F-W >> optimizer and see if they give improvement >> >> * you could implement an additional optimizer >> >> * you could implement an algorithm that is meant to use the F-W >> optimizer, like maybe some of the F-W SVM work that Jaggi also did? >> That might be too much for a single summer though... >> >> In either case, the choice is up to you---the project idea is there as >> kind of a boilerplate starting point for whatever ideas you would find >> most interesting. >> >> Thanks, >> >> Ryan >> >> -- >> Ryan Curtin | "Avoid the planet Earth at all costs." >> r...@ratml.org <mailto:r...@ratml.org> | - The President >> _______________________________________________ >> mlpack mailing list >> mlpack@lists.mlpack.org <mailto:mlpack@lists.mlpack.org> >> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack >> <http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack> > > _______________________________________________ > mlpack mailing list > mlpack@lists.mlpack.org > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
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