May 21, 2024, 4:44 a.m. | Matthew A. Price, Jason D. McEwen

cs.LG updates on

arXiv:2311.14670v2 Announce Type: replace-cross
Abstract: Many areas of science and engineering encounter data defined on spherical manifolds. Modelling and analysis of spherical data often necessitates spherical harmonic transforms, at high degrees, and increasingly requires efficient computation of gradients for machine learning or other differentiable programming tasks. We develop novel algorithmic structures for accelerated and differentiable computation of generalised Fourier transforms on the sphere $\mathbb{S}^2$ and rotation group $\text{SO}(3)$, i.e. spherical harmonic and Wigner transforms, respectively. We present a recursive algorithm …

abstract analysis and analysis arxiv computation cs.lg data differentiable engineering machine machine learning modelling novel physics.comp-ph programming replace science tasks type

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