Hi, It would be very useful if I got some feedback on this idea. Thank you,
Arjun. On Thu, Mar 15, 2018 at 7:51 PM, arjun k <[email protected]> wrote: > My idea was to create a framework for VAE where the user can specify an > architecture and we can integrate it with the current structure of mlpack. > This would be useful for many users that are concerned about the low-level > implementation details. Additionally, an option for using > pretrained network can be added for standard datasets. As mentioned testing > would be an issue as testing of generative models are not that evolved. > There is a concept of inception score which can be used for testing the > network. This type of testing would be useful for future implementation of > any generative models. I am still thinking about where in mlpack would this > be integrated into. > > Thanks, > Arjun. > > On Wed, Mar 14, 2018 at 11:29 AM, Marcus Edel <[email protected]> > wrote: > >> 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]> >> 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 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]> 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]> >>> 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/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? >>>> >>>> >>>> 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/DigitRec >>>> ognizer >>>> >>>> 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/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] >>>> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack >>>> >>>> >>>> >>> >>> >> >> >
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