Hello Arjun, thanks for the feedback on this one. Agreed, this could end up as an additional feature, I think we could keep that in mind for the interface if we like to go for it.
Thanks, Marcus > On 14. Mar 2018, at 03:03, arjun k <[email protected]> wrote: > > Hi, > > The paper looks interesting, the idea to introduce RL to relieve the lack of > data is good. But what I found is that it makes some assumptions about the > data that is that the latent representation can be divided into disentangled > and non-interpretable variable. Usually what happens is these assumptions do > not scale well to different data. Otherwise overall the model looks promising > and would be interesting implement. Maybe we could add this as a feature to > main VAE framework(like an alternative for use in semi-supervised learning > scenarios) since VAE as of itself is unsupervised. Let me know what you > think. Thank you, > > Arjun > > On Tue, Mar 13, 2018 at 7:29 PM, Marcus Edel <[email protected] > <mailto:[email protected]>> wrote: > Hello Arjun, > >> 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)? > > I think we could combine both ideas, something like > https://arxiv.org/abs/1709.05047 <https://arxiv.org/abs/1709.05047> could > work, let me know if that would is an > option you are interested in. > > Thanks, > Marcus > >> On 12. Mar 2018, at 23:45, arjun k <[email protected] >> <mailto:[email protected]>> wrote: >> >> 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] >> <mailto:[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 >>> <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 >> <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] >>> <mailto:[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/1802.07401 >>> <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? >>> >>> Thank you, >>> Arjun Karuvally, >>> College of Information and Computer Science, >>> University of Massachusetts, Amherst. >>> _______________________________________________ >>> 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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