5 citations · 11 across the 5 of their papers we have counts for
3 papers · 1 filter
Time Series Viewmakers for Robust Disruption Prediction
Dhruva Chayapathy, Tavis Siebert, Lucas Spangher +3
Machine Learning guided data augmentation may support the development of technologies in the physical sciences, such as nuclear fusion tokamaks. Here we endeavor to study the probl…
Offline Regularised Reinforcement Learning for Large Language Models Alignment
Pierre Harvey Richemond, Yunhao Tang, Daniel Guo +15
The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from…
Continuous Convolutional Neural Networks for Disruption Prediction in Nuclear Fusion Plasmas
William F Arnold, Lucas Spangher, Christina Rea
Grid decarbonization for climate change requires dispatchable carbon-free energy like nuclear fusion. The tokamak concept offers a promising path for fusion, but one of the foremos…