[mlpack] GSoC 2020 Ideas

[Andrei M]

Hello,

I'm a second year master's degree student in the field of artificial
intelligence and I've been thinking about applying to Google Summer of Code
for this summer and mlpack is the project I want to work on.

I've spent the last few weeks to get familiar with the code base and write
some code for a new feature (a loss function that wasn't implemented).
There are several ideas in the list that peaked my interest and I consider
them equally interesting: reinforcement learning, essential deep learning
modules, application of ANN algorithms implemented in mlpack and
improvisation and implementation of ANN modules.

I think these ideas would fit well for me since I've been implementing
neural networks such as DQN, Double DQN, Dueling networks, GANS and several
others in PyTorch and I've also been in touch with the state-of-art
research in various fields, like the ones mentioned above. Therefore, I
think I would equally enjoy working on the reinforcement learning path and
working on bringing features and modules that are present in other
libraries, like PyTorch.

Below are some summaries of the ideas I'm thinking about:
*Reinforcement learning*: Here I would like to work on Rainbow and Proximal
Policy Optimization Algorithms, train and test them on different
environments and empirically show their advantages and disadvantages (for
example how double DQN can reduce the overestimation problem that appears
in DQN).
*Application of ANN algorithms implemented in mlpack*: For this idea, I
have two options that come to my mind: first one is implementing a sequence
to sequence model for language translation and the other consists of
implementing U-Net like architectures which are usually employed for
segmentation tasks or depth prediction.
*Essential deep learning modules*: The plan I propose for this idea is
implementing some of the GAN architectures that aren't yet implemented,
starting from the first types of GANs that appeared, like conditional GANs
and info GANs, then advancing to more modern ones, trying to obtain and
visualize the results shown in the papers they've been presented on.

I would also like to know what are the features with high priority for
mlpack to have and if you have any suggestion on what I should propose to
match these priorities.

Also, can more ideas be proposed in a single application?
Any feedback and suggestions are appreciated.

Best,
Andrei
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