Hello Sagar, > Thanks a lot for the reply and also guiding me on the IRC(my nick is > sagarbhathwar). I am researching on ssRBM and once I am done with thoroughly > understanding it, I will finish up the proposal for ssRBM. Shall I put it for > feedback/review as soon as I complete ssRBM(+DBN) or should I also complete > the > proposal for Stacked GAN(which I think is our way forward for GANs) and then > put it up for review together? I shall follow according to your convinience
Your choice, note that we may not be able to comment timely on every draft and update. > P.S. - Is it a good idea to separate the project details into two parts? I think that is a good idea, looking forward to take a look at the proposal. I hope this is helpful, let us know if you have any further questions. Thanks, Marcus > On 27 Mar 2017, at 03:35, Sagar B Hathwar <[email protected]> wrote: > > Hi Marcus > > Thanks a lot for the reply and also guiding me on the IRC(my nick is > sagarbhathwar). > I am researching on ssRBM and once I am done with thoroughly understanding > it, I will finish > up the proposal for ssRBM. Shall I put it for feedback/review as soon as I > complete ssRBM(+DBN) > or should I also complete the proposal for Stacked GAN(which I think is our > way forward for GANs) > and then put it up for review together? I shall follow according to your > convinience > > P.S. - Is it a good idea to separate the project details into two parts? > First part - "The high level abstraction details" which talks about the > algorithm in abstracted > terms without going into coding the algorithm > Second part - "The low level implementation details" which talks about how > the code for the > algorithm adhers to mlpack API and can interact with existing API to make > full utilization of it. > > What do you think about the above approach? > > Thanks a lot for your time > Sagar B Hathwar > > On Sun, Mar 26, 2017 at 2:47 PM, Marcus Edel <[email protected] > <mailto:[email protected]>> wrote: > Hello Sagar, > > welcome and thanks for the interest. > > > I titled the project as "Generative Neural Networks - RBM and GAN". Any > > thoughts > > on this? > > Sounds good, note that the focus of the project is to take a look at recent > improvements/ideas, meaning that implementing e.g. standard RBM might be an > option but it should be implemented with the extensions in mind. > > > Can I also have some pointers on writing a proposal on this topic? I went > > through the guidelines on the wiki, but since this project involves > > implementing > > two algorithms, I would like to know the best way to go about writing a good > > proposal > > There are a lot of good proposals out there that might be helpful, the Student > Manual (https://developers.google.com/open-source/gsoc/resources/manual > <https://developers.google.com/open-source/gsoc/resources/manual>) also > links to some good examples. > > I hope this is helpful, let us know if you have any further questions. > > Thanks > Marcus > > > On 25 Mar 2017, at 20:16, Sagar B Hathwar <[email protected] > > <mailto:[email protected]>> wrote: > > > > Hello > > > > I looked at the Essential Deep Learning Modules under GSoC project ideas. > > Since generative models are a hot field today, I felt that implementing > > Restricted Boltzmann Machine(plus Deep Belief Networks) and Generative > > Adversarial Networks (for unsupervised learning) would be a good > > combination since both are generative but quite different. I went through > > the resources provided under the topic and also additional papers on it. > > Both these algorithms are very interesting. > > > > I titled the project as "Generative Neural Networks - RBM and GAN". Any > > thoughts on this? > > > > Can I also have some pointers on writing a proposal on this topic? I went > > through the guidelines on the wiki, but since this project involves > > implementing two algorithms, I would like to know the best way to go about > > writing a good proposal > > > > Thanks > > Sagar > > _______________________________________________ > > 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> > >
_______________________________________________ mlpack mailing list [email protected] http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
