Elias Nyholm
I am a doctoral student at the Division for Algebra and Geometry. I am part of the WASP-funded research project on Geometric deep learning and equivariant neural networks. My research generally lies in the intersection between mathematics, computer science and physics, where I study machine learning models from a mathematical perspective with focus on symmetries and equivariance. More often than not do my projects also include some application of these models to problems in physics, mathematics and autonomous driving.
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Equivariant non-linear maps for neural networks on homogeneous spaces
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