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 <marcus.e...@fu-berlin.de> 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 <guptavaibhav18...@gmail.com> > 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 > mlpack@lists.mlpack.org > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack > > >
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