Symbolic regression can be used to rapidly provide solutions to problems in science which may have large computational complexity or may even be intractable. It can be used to discover a symbolic expression describing data such as a physical law. Previous work has explored next-generation transformers[2][6] based on SymbolicGPT[3] for symbolic regression and, separately, using transformers to seed generative techniques such as genetic programming and reinforcement learning[1][4][5]. This project aims to combine recent developments in transformer models with generative frameworks. Projects which build upon previous projects will be given priority.
Total project length: 175/350 hours.
Significant experience with Transformer machine learning models in Python (preferably using pytorch). Experience with reinforcement learning, genetic programming, or other generative frameworks is preferred.
Intermediate
Please use this link to access the test for this project.
Please DO NOT contact mentors directly by email. Questions should instead be directed to ml4-sci@cern.ch which is forwarded to mentors. To submit your proposal, CV, and test task solutions, please use this Google form.