![]() ![]() References: Evolving Neural Networks through Augmenting Topologies, ensmallen: a flexible C++ library for efficient function optimization Relevant tickets: none are open at this time We suggest that everyone who wishes to apply for this idea, try to compile the source code and explore the source code. Recommendations for preparing an application: To be able to work on this you should be familiar with the source code of mlpack and mlpack's optimization framewwork ensmallen. NEAT must be implemented according to mlpack's optimization interface so that the method can work with different functions.ĭeliverable: Implemented NeuroEvolution of Augmenting Topologies (NEAT) and proof (via tests) that the algortihms works on different test functions. On the other end of the spectrum, NEAT was used in the MarI/O project written by Seth Bling, who was able to complete the first level of Super Mario World using NEAT. Researchers at Fermilab used NEAT to compute the most accurate current measurement of the mass of the top quark at the Tevatron Collider. There are many use cases for neuroevolution NEAT has been used in many fields, from physics calculations to game development. NeuroEvolution of Augmenting Topologies (NEAT) can evolve networks of unbounded complexity from a minimal starting point, by searching the search space of simple networks and then looks at increasingly complex networks, which is called complexification. Mario Kart 64 by Nick Nelson and MarI/O by Seth Bling Instead, specific actions are rewarded or discouraged. Neuroevolution is a form of reinforcement learning, meaning that incoming data is raw and not marked or labeled. Potential mentor(s): Marcus Edel, Mikhail Lozhnikov, Shikhar Jaiswal, Saksham Bansal NeuroEvolution of Augmenting Topologies References: Deep learning reading list, Deep learning bibliography When you prepare your application, provide some comments/ideas/tradeoffs/considerations about your decision process, when choosing the models you want to implement over the summer. Take a look at the different layers and basic network structures. We suggest that you build mlpack on your system and explore the functionalities. Recommendations for preparing an application:īeing familiar with the mlpack codebase, especially with existing neural network code is the first step if you wish to take up this task. Necessary knowledge: a working knowledge of what neural networks are, willingness to dive into some literature on the topic, basic C++ GAN: Improved Training of Wasserstein GANs, PacGANĭeliverable: Implemented deep learning modules and proof (via tests) that the code works.RBFN: Back to the Future: Radial Basis Function Networks Revisited, Learning methods for radial basis function networks.Note this project aims to revisit some of the traditional models from a more modern perspective e.g.: The architecture should be designed to build a foundation to integrating many more models including support for other state-of-the-art deep learning techniques. This could include Deep Belief Networks (DBN), Radial Basis Function Networks (RBFN) and Generative Adversarial Networks (GAN). ![]() A good project will select some of the architectures and implement them (with tests and documentation) over the course of the summer. This project involves implementing essential building blocks of deep learning algorithms based on the existing neural network codebase. For example, DeepMind has shown that neural networks can learn to play Atari games just by observing large amounts of images, without being trained explicitly on how to play games.
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