[mlpack] GSOC 2018 [Essential Deep Learning Modules]

[bansa031 University of Minnesota]

Hi,

I am Ayush Bansal, a second-year master's student at University of
Minnesota, USA. I have a keen interest in the field of Deep Learning and
Machine Learning. I have been using and exploring various deep learning
models and techniques and am very interested in working on Generative
Adversarial Networks. I have worked on them for my project last semester
especially in the framework of Capsule Networks. If possible I would like
to work on the proposed idea of either Stacked Generative Adversarial
Networks or Improved Techniques for Training GANs. I would love to increase
my knowledge in the field of GANs and possibly other models in Deep
Learning.

If allowed I would also like to implement Capsule Networks in MLPack. All
in all, I would like you to consider my ideas and give me guidance on this
project.

Thank You
-- 
Ayush Bansal
University of Minnesota - Twin Cities - Class of 2018
Mathematics and Computer Science Major
bansa...@umn.edu | ayushb...@gmail.com
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Re: [mlpack] Mlpack Gsoc Proposal

[Marcus Edel]

Hello Tiberiu,

thanks for getting in touch. 

> I use deep learning approaches in my homework for the university or in my
> personal projects (for example I have developed a chat bot that integrates nlp
> and ml for the competition The Voice).

That sounds awesome, is the code public, did you wrote everything from scratch
or did you use a framework?

> I was thinking that i can do over the summer RBFN or BRN or even LSTM.


Sounds good, note we already have an LSTM implementation, that can be used.
However, there are many LSTM related ideas that could be explored like Nested
LSTMs.

> Also i think that it will be interesting to do
> a implementation of the colorful image colorization and will be suited for
> bringing newcomers into the project.

That sounds like a great idea to me, I guess this could be a neat side project
to get familiar with the codebase since it's mainly a conv net, most parts
should already exist.

Let me know if I should clarify anything.

Thanks,
Marcus

> On 14. Feb 2018, at 11:37, Lepadatu Tiberiu Andrei 
>  wrote:
> 
> Hi,
> 
> I am Tiberiu Lepadatu, a second year student at the University Politehnica of
> Bucharest. I have a strong interest in machine learning and numerical 
> methods. I
> use deep learning approaches in my homework for the university or in my 
> personal
> projects (for example I have developed a chat bot that integrates nlp and ml 
> for
> the competition The Voice). I am currently enrolled in the machine learning
> course by Andrew NG and i am an avid learner. I have taken the advice that was
> given to me on the IRC channel and i have devoted my time in the past weeks
> understanding the neural networks implementation from your library. GSOC will
> give me the write enforcement to develop myself as a student in mashing Lenin
> and as a researcher in this field. I will be more than happy to help you
> implement the deep learning modules. I was thinking that i can do over the
> summer RBFN or BRN or even LSTM. Also i think that it will be interesting to 
> do
> a implementation of the colorful image colorization and will be suited for
> bringing newcomers into the project. All in all, I want you to consider my 
> ideas
> and to give me the proper guidance for this project. Also giving the research
> intensive character of the study that i am proposing i feel like my first pull
> request tot he project will be months in the future.
> 
> Kind regards,
> Tiberiu Lepdatu
> 
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[mlpack] Mlpack Gsoc Proposal

[Lepadatu Tiberiu Andrei]

Hi,

I am Tiberiu Lepadatu, a second year student at the University Politehnica
of
Bucharest. I have a strong interest in machine learning and numerical
methods. I
use deep learning approaches in my homework for the university or in my
personal
projects (for example I have developed a chat bot that integrates nlp and
ml for
the competition The Voice). I am currently enrolled in the machine learning
course by Andrew NG and i am an avid learner. I have taken the advice that
was
given to me on the IRC channel and i have devoted my time in the past weeks
understanding the neural networks implementation from your library. GSOC
will
give me the write enforcement to develop myself as a student in mashing
Lenin
and as a researcher in this field. I will be more than happy to help you
implement the deep learning modules. I was thinking that i can do over the
summer RBFN or BRN or even LSTM. Also i think that it will be interesting
to do
a implementation of the colorful image colorization and will be suited for
bringing newcomers into the project. All in all, I want you to consider my
ideas
and to give me the proper guidance for this project. Also giving the
research
intensive character of the study that i am proposing i feel like my first
pull
request tot he project will be months in the future.

Kind regards,
Tiberiu Lepdatu
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Re: [mlpack] [GSoC'18] Reinforcement Learning

[Marcus Edel]

Hello Vaibhav,

welcome, thanks for getting in touch.

> My research domain is Artificial Intelligence, specifically Reinforcement
> Learning & Multi-agent Systems in Machine Learning Lab, IIIT Hyderabad. I am
> doing my research under Prof. Praveen Paruchuri and Prof. Balaraman 
> Ravindaran.
> I have past open source experience of contributing to ZENODO(CERN). Also, I 
> was
> selected as intern in The Linux Foundation where my project revolved around
> coming up with various performance metrics for object storage.

That's sounds really interesting, what did you do at ZENODO, if you don't mind
to share that information.

> I have gone through the project idea list of mlpack and found the project idea
> Reinforcement Learning really interesting. I have read papers on Double DQN /
> Playing Atari with deep reinforcement learning and have fairly good
> understanding of these. Attached is the exhaustive list of papers that I have
> implemented and read as part of research work.  I am an enthusiast in
> reinforcement learning and am ready to read and learn on the go as the need 
> be.
> 
> Since I am new to mlpack please let me know as to how can I get started. Also
> since, there are no relevant tickets open at this time, please suggest me know
> how to proceed.

Getting familiar with the codebase especially
src/mlpack/methods/reinforcement_learning/ should be the first step. Running the
tests: (rl_components_test.cpp) 'bin/mlpack_test -t RLComponentsTest' and
(q_learning_test.cpp) 'bin/mlpack_test -t QLearningTest' should help to
understand the overall structure.

If you like you can work on a simple RL method like (stochastic) Policy
Gradients and use that to jump into the codebase, but don't feel obligated.

Also, the methods listed on the ideas page are just suggestions, so if you have
an interesting method in mind you like to work on, let me know.

Thanks,
Marcus

> On 13. Feb 2018, at 22:17, VAIBHAV GUPTA  wrote:
> 
> Hello everyone,
> 
> My name is Vaibhav Gupta. I am a 3rd year undergraduate student pursuing my 
> B.Tech in Computer Science and M.S by research in IIIT Hyderabad, India. 
> 
> My research domain is Artificial Intelligence, specifically Reinforcement 
> Learning & Multi-agent Systems in Machine Learning Lab, IIIT Hyderabad. I am 
> doing my research under Prof. Praveen Paruchuri 
>  and Prof. 
> Balaraman Ravindaran 
> . I have past 
> open source experience of contributing to ZENODO(CERN). Also, I was selected 
> as intern in The Linux Foundation where my project revolved around coming up 
> with various performance metrics for object storage.
> 
> I have good understanding of neural networks and (as a part of my academic 
> project). I have also implemented 
>  the 
> paper - Distilling the knowledge in Neural Network 
>  in which we try to transfer the learning 
> of a larger network(teacher) to a relatively smaller network(student) making 
> use of the logits of the teacher network. 
> 
> Currently, I am doing research in Reinforcement learning (Transfer learning) 
> and trying to come up with a state granular confidence metric in 
> simultaneously learning heterogeneous agents. I have sound knowledge of many 
> prominent algorithms used in Reinforcement Learning.
> 
> I have a sound background in data structures and algorithms and have 
> qualified twice for ACM ICPC regionals. I have secured good rank in other 
> programming contests too. I have good understanding of C++ having done all my 
> competitive programming and several different projects using it.
> 
> I have gone through the project idea list of mlpack and found the project 
> idea Reinforcement Learning really interesting. I have read papers on Double 
> DQN /  Playing Atari with deep reinforcement learning and have fairly good 
> understanding of these. Attached is the exhaustive list of papers that I have 
> implemented and read as part of research work.  I am an enthusiast in 
> reinforcement learning and am ready to read and learn on the go as the need 
> be.
> 
> Since I am new to mlpack please let me know as to how can I get started. Also 
> since, there are no relevant tickets open at this time, please suggest me 
> know how to proceed.
> 
> Thanks 
> Vaibhav Gupta
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