GSoC 2017 just started today, We are very excited to work with 10 awesome students over the summer. Here is what they will be working on this summer:
Dewang Sultania, mentored by
Ryan Curtin and
Dewang will improve the existing benchmarking system we have in place by implementing and benchmarking current
mlpack methods as well as other competetive implementations and libaries. He will also work on a makeover of the visual appeal by introducing new ideas to make the interface more accessible.
Dewang is an undergrad student at Birla Institute of Technology, interested in algorithms, data structures, and database management systems. When he has free time, he likes to dance, read sci-fi novels and is the guy you have to ask if you like rafting.
Kirill Mishchenko, mentored by
Kirill will design and develop modules for cross-validation and hyper-parameter tuning in
mlpack. Specifically, Kirill will create three separate modules for working with different
mlpack models and methods: (1) cross-validation (the module can split a training set into some number of folds and evaluate some average measure over all folds.); (2) hyper-parameter tuning (the module take a method and tests different values for the training parameters).
Kirill is a Ph.D. student at the Ural Federal Univercity (Ekaterinburg, Russia); he likes to walk and cycle with friends.
Kris Singh, mentored
Kris will extend the existing network implementation to support RBM that support Spike and Slab RBM’s, and then implement a system of two neural networks competing against each other in a zero-sum game framework called Generative adversarial networks (GANs). Then, he will benchmark the
mlpack implementation against other implementations using the
mlpack benchmarking system.
Kris is a master student at the Indian Institute of Technology, Hyderabad; he likes travel and has been to almost 14 out of 29 in India, which is a lot if you compare it against other countries like Germany which is about 9 times smaller than India, okay United States is about 3 times bigger than India, but who has seen all US states.
Chenzhe Diao, mentored
Chenzhe will work on an implementation of Frank-Wolfe for the various atomic norms listed in Jaggi's awesome paper. Moreover, he will test the pieces on real world problems such as dictionary learning that will call the solver, to give a further demonstration and comparison of the implementation.
Chenzhe is a Ph.D. student at the University of Alberta; he is far from the only University of Alberta student who has worked with
mlpack in the past, as he is the third student we have accepted from there. Chenzhe enjoys skiing like a true Canadian.
Konstantin Sidorov, mentored by
Marcus Edel and
Konstantin will implement the components of the Hierarchical Attentive Memory architecture (HAM) including Highway Networks and test it on various tasks. The pieces of this architecture will be usable for other neural network applications and will open the possibilities to train even longer sequences.
Konstantin a first-year student in Software Engineering at Astrakhan State University, interested in playing Go - the ancient Chinese game, you may heard of it in the news e.g. "AlphaGo retires from competitive Go after defeating world number one 3-0", I think Konstantin is one of the few members that can follow and understand the game.
Saurabh Gupta, mentored by
Saurabh will redesign and automate the
mlpack build process. Specifically, Saurabh will (1) complete the build matrix (testing
mlpack on different compilers, different architectures, different Boost versions); (2) running docker with Jenkins (continuous integration and continuous deployment using plugins to automate the build and deploy process) (3) macOS and Windows builds (testing
mlpack on windows and macOS).
Saurabh is an undergraduate (B. Tech.) majoring in Information Technology and Mathematics at the University of Delhi. He likes adventure sports and wandering mountains maybe in the future we can together climb one of the seven summits, probably we don't start with Everest, but who knows.
Kartik Nighania, mentored by
Kartik extends and implements the neuroevolution algorithms for the neural network framework in
mlpack, such as CNE, NEAT, and CMAES. These will be tested on various problems, including applying all these algorithms to NES games. In addition, benchmarking will be done to verify that
mlpack's implementations are competitive with—or faster than—other implementations of these neuroevolution algorithms. If time permits he will also work the visual appeal of the architecture and results.
Kartik is an undergrad student at the National Institute of Technology, Surat, India; he likes robotics (embedded systems and computer vision), making drones and playing computer games. Make sure to checkout his website and his blog.
Sumedh Ghaisas, mentored by
Ryan Curtin and
Sumedh will implement the components of the Neural Turing Machine (NTM) including various tricks to imrpove the overall training process like batch normalization. The pieces of this architecture will be usable for other neural network applications and will open the possibilities just like the HAM idea to train even longer sequences.
Sumedh is a master student at the University of Edinburgh; he likes playing table tennis in his free time and enjoys listening to the blues.
Shikhar Bhardwaj, mentored by
Shikhar will implement parallel stochastic optimization methods in
mlpack, namely parallel stochastic gradient descent based on HOGWILD and asynchronous parallel stochastic coordinate descent based on AsySCD, considering various available approaches like OpenMP for better efficiency and performance.
Shikhar is an undergraduate student from Delhi, India, studying Mathematics and Computing Engineering at DTU; He likes to tinker with electronics, everything from "blink an LED with a 555 timer" to "set up a microcontroller with few sensors", looks like this year we have two students who know a lot about all this little building blocks that enable us to write code on a computer instead of using pen and paper.
Shangtong Zhang, mentored by
Shangtong will implement a Reinforcement Learning framework and will compare the implementations against other competetiv methods. Specifically, Shangtong will work on (1) DQN (Double DQN algorithm - adds stability to the learning by using a Q evaluation); (2) A3C (Asynchronous Actor-Critic Agents is probably the universal agent that can be used as a stable baseline for solving various tasks)
Shangtong is a master student at the University of Alberta; he likes to run, and if I say run I don't talk about run around the block a few times, no he runs 21-43 km (half marathon/marathon).
You can find more information on each of the projects on the Summer of Code website.
Anyway, congratulations to
Shangtong! The coding period starts today (May 30).
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