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