[mlpack] GSoC 2020 Ideas

[Marcus Edel]

Hello Andrei,

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.

Sounds totally reasonable to me.

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

I like both models, also good that you mentioned you like to focus on either
U-Net or DeepLabv3.

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

Definitely, a big part of each project is documentation and testing, writing
good tests takes time.

Let me know if I should clarify anything further.

Thanks,
Marcus

> On 10. Mar 2020, at 15:50, Andrei M <mihalea.andrei at gmail.com> wrote:
> 
> 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 <mailto: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 <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 <mailto: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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