[mlpack] GSoC - 2017

[Prasanna Patil]

Hi Marcus,

Thanks for your response.

I've linked some recent papers on the ideas pages, that revisit some of the
> traditional models like "Spike and slab restricted boltzmann machine" by
> Courville et al or "Back to the Future: Radial Basis Function Networks
> Revisited" by Que and Belkin paper. There are a couple more references for
> the
> mentioned models, especially for GAN there are some really interesting
> papers
> that are definitely worth a look.
>


Yup I checked them out. Currently I am reading the "Back to the Future:
Radial Basis Function Networks Revisited" paper. I am planning to go
through all of them in coming month. Also one of the papers in RBF sections
is paid so just wanted to check if you have some other source for learning
methods of RBF networks.


As stated in the project description, all models mentioned are just examples
> that could be implemented, we don't expect you to implement all models or
> any of
> the models. Meaning if you like to implement another network model that
> mlpack
> currently doesn't have, we are open to discuss the ideas with you.
>


So basically it is up to participant to decide which model he will be
implementing during project? If that's the case then how much models you
guys are expecting to be implemented during 3 months of project
(implementing models + documenting them)? I don't have first hand
experience with implementing models in actual open source library so I
can't estimate days needed to implement a well tested model.

Also is reading research papers part of the project or something to be done
before project period starts? This will help me to get my expected timeline.


If you like to implement machine learning algorithms that mlpack currently
> doesn't have, contributions like that are more than welcome. If you can
> provide
> a fast, well-tested implementation it would be great to include it in
> mlpack.
> Also, we are open for own project ideas, however, we have to make sure it's
> enough work and someone is able to mentor the project. The GRU layer and
> batch
> norm is definitely an interesting idea that when extended by some other
> methods
> could build up an interesting GSoC project.
>


That's great. Let me research about other methods I can implement alongside
GRU and batch norm so that it can turn into proper GSoC project. If I can
get enough work to do with this proposal I will go with my own project
otherwise I will apply for Essential Deep Learning Modules.


I'll see if I can open some ann related issues, that could be a great start
> to
> dive into the codebase.
>


That's great. Familiarising myself with mlpack codebase is certainly hard
without working with it and getting hand dirty. I am looking forward to
issues related with ann. Until then I will try to understand mlpack coding
style and read research papers referenced on project ideas page. I
apologise if any of the questions I asked above are inappropriate.

Thanks,
Prasanna
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://knife.lugatgt.org/pipermail/mlpack/attachments/20170211/2280f40b/attachment.html>