[mlpack] GSoC 2017: Reinforcement Learning and Essential Deep Learning Modules

Abhinav Moudgil abhinavmoudgil95 at gmail.com
Tue Mar 28 16:01:17 EDT 2017


Hi,

I have shared my proposal draft with you on Google Summer of Code website.
Please review and suggest edits, if you have time.

Thank you very much.

Kind regards,
Abhinav Moudgil

On Sun, Mar 26, 2017 at 7:07 PM, Marcus Edel <marcus.edel at fu-berlin.de>
wrote:

> Hello Abhinav,
>
> sounds good, we look forward to your proposals.
>
> Thanks,
> Marcus
>
> On 24 Mar 2017, at 19:00, Abhinav Moudgil <abhinavmoudgil95 at gmail.com>
> wrote:
>
> Hi Marcus,
>
> I am glad to hear back from you. Taking a detailed look at both the
> projects, I have decided to go ahead with the second project i.e.
> "Essential Deep Learning Modules". Just to reiterate, I am planning to
> implement GAN, BRN and RBM. I will draft a proposal and share it with you
> on Google Summer of Code website for feedback.
>
> Kind regards,
> Abhinav Moudgil
>
> On Fri, Mar 24, 2017 at 10:25 PM, Marcus Edel <marcus.edel at fu-berlin.de>
> wrote:
>
>> Hello Abhinav,
>>
>> thanks for getting in touch and welcome.
>>
>> I would love to contribute to mlpack during this summer. It would be
>> great if
>> you could elaborate your views on the above projects. Looking forward to
>> your
>> guidance.
>>
>>
>> That are some really neat project's you listed above. I think Ryan tried
>> something "similar" as training a language model on a jokes corpus.
>> Anyway here
>> are my two cents to the projects mentioned above, I think each project is
>> equally interesting and depending on what you like to do equally
>> difficult and
>> at the same time rewarding. The intention behind each project is to work
>> on
>> recent ideas and to provide a fast implementation at the end of the
>> summer. At
>> the end I can't help you with the decision since you worked on each topic
>> it's
>> even difficult to give you a recommendation.
>>
>> I hope something I said was helpful,
>>
>> Thanks,
>> Marcus
>>
>> On 23 Mar 2017, at 16:25, Abhinav Moudgil <abhinavmoudgil95 at gmail.com>
>> wrote:
>>
>> Hi,
>>
>> I am Abhinav Moudgil, a senior undergraduate research student in Deep
>> Learning and Computer Vision, working on PR #942
>> <https://github.com/mlpack/mlpack/pull/942>. I went through mlpack
>> project ideas
>> <https://github.com/mlpack/mlpack/wiki/SummerOfCodeIdeas#essential-deep-learning-modules>
>> and I found the following two projects really interesting (in preference
>> order) for Google Summer of Code 2017:
>>
>> *1. Reinforcement Learning (RL)*
>> It would be a great learning experience for me to implement RL
>> algorithms, which are fast and scalable. Previously, I have studied various
>> RL algorithms well like Monte Carlo Policy Gradient (PG) with REINFORCE
>> <https://gist.github.com/abhinavmoudgil95/138db4c55c42f91f4c858294acadb771>,
>> Deep Q-learning (for discrete and continuous state space), Deep
>> Deterministic PG with Actor-Critic networks, Policy Iteration for Maze
>> environment, Hill Climbing
>> <https://gist.github.com/abhinavmoudgil95/108123c880488965b8c1744cacd60dd6>,
>> Random Search
>> <https://gist.github.com/abhinavmoudgil95/6fcb2db7314e6c4f6b7a028dfe1f27db> etc.
>> I have implemented and tested them in Python using Tensorflow. My OpenAI
>> gym profile is accessible here
>> <https://gym.openai.com/users/abhinavmoudgil95>. I will open source all
>> my RL codes in a separate repository soon.
>>
>> *2. Essential Deep Learning Modules*
>> I have studied the relevant literature for this project in the past and I
>> like converting mathematical equations from research papers to code. In
>> Summer 2016, I worked on feature engineering as a Google Summer of Code
>> project <https://abhinavmoudgil95.github.io/2016-08-23/gsoc-conclusion/>
>> with CERN SFT where I worked on some advanced feature extraction methods
>> like Deep Autoencoders, Feature Clustering, Hessian Locally Linear
>> Embedding etc. So, I explored literature on Restricted Boltzmann Machines,
>> Hopfield Networks etc. In this project, I would like to implement the
>> following models:
>>
>>    - RBM - Studied extensively during my Google Summer of Code, 2016.
>>    - GAN - This semester, I am a Teaching Assistant for the course
>>    Statistical Methods in AI at my university IIIT-H
>>    <https://www.iiit.ac.in/>. As a part of this job, I am mentoring
>>    projects like Coupled GANs, Conditional GANs. I have studied the GAN
>>    literature well along with its variations like DCGANs, Improved Techniques
>>    for training GANs by OpenAI, Class Conditional GANs by Yann Lecun etc.
>>    - BRN - I solved
>>    <https://abhinavmoudgil95.github.io/2017-03-01/funnybot/> OpenAI
>>    Request for Research problem #2
>>    <https://openai.com/requests-for-research/#funnybot>. For that, I
>>    studied Recurrent Neural Networks in detail along with variations of it
>>    like LSTMs, Attention Models, BRNs. Currently, I am working on OpenAI
>>    Request for Research #3
>>    <https://openai.com/requests-for-research/#im2latex> which involves
>>    implementing Attention Models and Bidirectional RNNs.
>>
>> *Open Source Experience: *
>> I worked <https://github.com/abhinavmoudgil95/gsoc-2016> with CERN SFT
>> on feature engineering module as a Google Summer of Code student. I
>> contributed
>> <http://wiki.opencog.org/wikihome/index.php/Special:Contributions/Amod95>
>> to OpenCog foundation by fixing several bugs and writing an installation
>> script <https://github.com/opencog/ocpkg/pull/50> for Mac OS X. I also
>> contributed to Shogun, a Machine Learning toolbox where I worked on
>> improving and benchmarking
>> <https://github.com/shogun-toolbox/shogun/issues/3048> basic ML
>> algorithms like PCA, LDA etc.
>>
>> I would love to contribute to mlpack during this summer. It would be
>> great if you could elaborate your views on the above projects. Looking
>> forward to your guidance.
>>
>> Kind regards,
>>
>> Abhinav Moudgil
>> Github: https://github.com/abhinavmoudgil95
>> Website: https://abhinavmoudgil95.github.io/
>> LinkedIn: https://www.linkedin.com/in/abhinavmoudgil/
>>
>>
>>
>
>
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