Hello Vaibhav, > As far as Zenodo is concerned, I was majorly involved in discussions regarding > the design (both UI and backend) of the Researchers's Profile Project - to be > rendered using a dedicated page. It involved proposing various possible > Database > Schemas, UI Designs via mockups and proposed various mechanism for message > passing by juxtaposing their merits/demerits. I also resolved some issues > within > the code-base.
That sounds like a lot of fun, thanks for the update on this. > Currently, I am going through the codebase of MLPACK to better understand the > code structure along with present algorithms & implementations. This would > help > to gain some insights as to What all algorithms can be added straightaway ? , > Does the existing implementations have scope of improvement? (and similar > questions). There is an open PR: https://github.com/mlpack/mlpack/pull/1091 that might be interesting, I should point out that this is not an easy one, but any progress on that would be great. Let me know if I should clarify anything. Thanks, Marcus > On 15. Feb 2018, at 21:50, VAIBHAV GUPTA <[email protected]> wrote: > > Hi Marcus, > > Thanks for the response. > > What did you do at ZENODO? > > As far as Zenodo is concerned, I was majorly involved in discussions > regarding the design (both UI and backend) of the Researchers's Profile > Project - to be rendered using a dedicated page. It involved proposing > various possible Database Schemas, UI Designs via mockups and proposed > various mechanism for message passing by juxtaposing their merits/demerits. I > also resolved some issues within the code-base. > > Currently, I am going through the codebase of MLPACK to better understand the > code structure along with present algorithms & implementations. This would > help to gain some insights as to > What all algorithms can be added straightaway ? , > Does the existing implementations have scope of improvement? (and similar > questions). > > Thank you > Vaibhav > > > On Wed, Feb 14, 2018 at 3:38 PM, Marcus Edel <[email protected] > <mailto:[email protected]>> wrote: > 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 <[email protected] >> <mailto:[email protected]>> 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 >> <https://scholar.google.com/citations?user=ILUqgKEAAAAJ&hl=en> and Prof. >> Balaraman Ravindaran >> <https://scholar.google.co.in/citations?user=nGUcGrYAAAAJ&hl=en>. 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 >> <https://github.com/guptavaibhav18197/student-teacher-transfer-learning> the >> paper - Distilling the knowledge in Neural Network >> <https://arxiv.org/abs/1503.02531> 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 >> <Honours Project.pdf>_______________________________________________ >> 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> >
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