[mlpack] GSoC 2020 Ideas

[Andrei M]

Hello,

Thank you for the response.

I've been thinking more about the ideas for the GSoC and I've established a
top 2 I'd like to work on: Reinforcement learning or Applications of ANN.
(I'll only select one for the final proposal)

1. RL: I've taken a more in-depth look on the reinforcement learning
module. The DQN, Double DQN and prioritized replay are already implemented,
so as part of the rainbow the remaining components are Dueling networks,
Multi-step learning, Distributional RL, Noisy. Therefore, I suggest
finishing the implementation of the Rainbow DQN and then an implementation
of the ACKTR algorithm.

2. Applications of ANN: Implementing a U-Net or DeepLabv3 architecture for
semantic segmentation.

I would like to know if the ideas above would make enough for a summer
project for each of the two sections.

Thank you,
Andrei

On Mon, Mar 9, 2020 at 1:22 AM Marcus Edel <marcus.edel at fu-berlin.de> wrote:

> Hello Andrei,
>
> welcome and thanks for you interest. Looks like you already brainstormed
> about
> the ideas, that great. I think each method you proposed made sense, there
> is
> alrady a PR open for PPO (https://github.com/mlpack/mlpack/pull/1912)
> which is
> very close to being merged, so I think you can remove that from the list.
>
> Also, I think both ideas could be combined, like if you add a new layer to
> the
> codebase. That said, we don't have project priorities, so you a free to go
> with
> anything you find interesting.
>
> Let me know if I should clarify anything.
>
> Thanks,
> Marcus
>
> On 6. Mar 2020, at 15:47, Andrei M <mihalea.andrei at gmail.com> wrote:
>
> 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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>
>
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