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 <[email protected]> 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 > _______________________________________________ > mlpack mailing list > [email protected] > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
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