Hi Ryan, Thanks for your reply.I also went through the project offerings on the GSoC'19 page and the project 'Quantum Gaussian Mixture Models' fascinated me a lot.
I have already implemented GMM(full batch) and it's online version(stepwise EM) from scratch on MNIST dataset. Please look at my github account <https://github.com/subham913?tab=repositories>. I have already gone through the suggested paper(Quantum Clustering and Gaussian Mixture).Apart from what already has been suggested on the ideas page I would also try implementing its scalable version.Also as of General Purpose EM and Blackbox VI,I also plan to work on them.Please let me know if it would be feasible in the given time frame. Thanks and regards, Subham Kumar Undergraduate Student Computer Science and Engineering Indian Institute of Technology, Kanpur On Mon, Mar 18, 2019, 21:12 Ryan Curtin <r...@ratml.org> wrote: > On Thu, Mar 14, 2019 at 10:11:41PM +0530, Subham Barnwal wrote: > > I would love to build some "Bayesian Methods Based Libraries" which i > guess > > except TensorFlow probability people don't have too much options.Some of > > the things I am planning is to implement BlackBox VI,GP with > > scalability,EM(I see GMM is already there but may be I can try coding a > > more general purpose EM) etc.I am open to any other suggestions (Both > > bayesian and non-bayesian).I would appreciate your guidance on the > > same.Please help me over the same. > > Hi Subham, > > Thanks for getting in touch and the ideas that you've proposed look > interesting to me. If you prepare a proposal for these things, probably > one of the most important things to focus on is the API that is provided > to users for these techniques. > > Specifically for a general-purpose EM implementation, it would be great > if we could use that in place of the existing EM implementation used for > GMMs. You can take a look at the `EMFit<>` class in > src/mlpack/methods/gmm/em_fit.hpp to see how it's currently structured. > > Code reuse is a big priority for mlpack, so it would be good to take a > look through the library and see what components you could reuse or > adapt for your implementations. :) > > Hope this is helpful! Let me know if I can clarify anything. > > Thanks, > > Ryan > > -- > Ryan Curtin | "So, it's just you 57 punks against KUNG FU JOE?" > r...@ratml.org | - Kung Fu Joe >
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