---------- Forwarded message ---------- From: arjun k <[email protected]> Date: Mon, Mar 12, 2018 at 6:45 PM Subject: Re: [mlpack] Variational Autoencoders and Reinforcement Learning To: Marcus Edel <[email protected]>
Hi, Thank you, Marcus, for the quick reply. That clarifies the doubts I had. I am interested in both the projects, reinforcement learning and variational autoencoders with almost equal importance to both. So is there any way that I can involve in both the projects. Maybe focus on one and have some involvement in the other?. In that case, how would I write a proposal to this effect(write as two separate proposals or mention my interest in both under one proposal)? On Mon, Mar 12, 2018 at 9:41 AM, Marcus Edel <[email protected]> wrote: > Hello Arjun, > > welcome and thanks for getting in touch. > > I am Arjun, currently pursuing my Master's in Computer Science at the > University > of Massachusetts, Amherst, I came across the project on variational > autoencoders > and Reinforcement learning project and they look very interesting. Hope I > am not > too late. > > > The application phase opens today, so you are not too late. > > I am more interested in the reinforcement learning project as it involves > some > research in a field that I am working on and would like to get involved. > As I > understand, coding up an algorithm and implementing it in a single game > would > not be much of an issue. How many algorithms are proposed to be benchmarked > against each other? Is there any new idea that is being tested or the > research > component is the benchmark alone? > > > Keep in mind each method has to be tested and documented, which takes > time, so > my recommendation is to focus on one or two (depending on the method). The > research component is two-fold, you could compare different algorithms or > improve/ extend the method you are working on e.g. by integrating another > search > strategy, but this isn't a must, the focus is to extend the existing > framework. > > In the variational encoders I am quite familiar with generative modeling > having > worked on some research projects myself(https://arxiv.org/abs/1802.07401), > As we > can make variational encoders is just a training procedure, how abstracted > are > you intending the implementation to be. Should the framework allow the > user to > be able to customize the underlying neural network and add additional > features > or is it highly abstracted with no control over the underlying > architecture and > only able to use VAE as a black box? > > > Ideally, a user can modify the model structure based on the existing > infrastructure, providing a black box, is something that naturally results > from > the first idea. And could be realized in the form of a specific model > something > like: https://github.com/mlpack/models/tree/master/Kaggle/DigitRecognizer > > I hope anything I said was helpful, let me know if I should clarify > anything. > > Thanks, > Marcus > > On 11. Mar 2018, at 22:23, arjun k <[email protected]> wrote: > > Hi, > > I am Arjun, currently pursuing my Master's in Computer Science at the > University of Massachusetts, Amherst, I came across the project on > variational autoencoders and Reinforcement learning project and they look > very interesting. Hope I am not too late. > > I am more interested in the reinforcement learning project as it involves > some research in a field that I am working on and would like to get > involved. As I understand, coding up an algorithm and implementing it in a > single game would not be much of an issue. How many algorithms are proposed > to be benchmarked against each other? Is there any new idea that is being > tested or the research component is the benchmark alone? > > In the variational encoders I am quite familiar with generative modeling > having worked on some research projects myself(https://arxiv.org/abs/1 > 802.07401), As we can make variational encoders is just a training > procedure, how abstracted are you intending the implementation to be. > Should the framework allow the user to be able to customize the underlying > neural network and add additional features or is it highly abstracted with > no control over the underlying architecture and only able to use VAE as a > black box? > > Thank you, > Arjun Karuvally, > College of Information and Computer Science, > University of Massachusetts, Amherst. > _______________________________________________ > mlpack mailing list > [email protected] > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack > > >
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