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 <[email protected]> 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 <[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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