[mlpack] GSoC - 2017

[Prasanna Patil]

Hi Marcus,


> Sounds great especially maxout is interesting, let us know if you need any
> help
> with the implementation.
>

Sure. I have nearly finished on PReLU implementation. After that I will get
to maxout units.


> Sorry, I didn't realize that one of the papers isn't accessible for free,
> here
> are two more papers that are interesting:
>

Not an issue.


> "An improved radial basis function neural network for object image
> retrieval” by
> G. A. Montazer et al. - https://pdfs.semanticscholar.org/2f62/
> ff139763c697674e447825bc214653b97192.pdf
>
> "A Comparison of RBF Neural Network Training Algorithms for Inertial
> Sensor Based Terrain Classification” by
> T. Kurban et al. - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3312446/
>

Thanks for the updated links.


> That highly depends on the models you like to implement e.g. implementing
> Bidirectional Recurrent networks is straightforward since there is already
> an
> RNN class that can be used to construct the model or if you like to
> implement
> RBMs the step to DBN's is also fairly straightforward since you can stack
> RBM's
> to build you DBN. On the other side the implementation of the main idea
> might be
> straightforward but for each model, there are a bunch of interesting
> features
> that can be explored like different training methods so that you could
> easily
> work entirely on a single model.
>
> I would recommend that when you write your proposal; define some main
> goals that
> you think are realistic to achieve and probably add some other features
> that you
> like to work on if there is time left.
>

Thanks for the clarification. I will submit PR when I have finished
implementing PReLU completely.

Thanks,
Prasanna
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