WebConvolutional neural networks (CNNs) constructed natively on the sphere have been developed recently and shown to be highly effective for the analysis of spherical data. While an efficient framework has been formulated, spherical CNNs are nevertheless highly computationally demanding; typically they cannot scale beyond spherical signals of ... WebConvolutional neural networks (CNNs) constructed natively on the sphere have been developed recently and shown to be highly effective for the analysis of spherical data. …

GAUGE EQUIVARIANT SPHERICAL CNN - OpenReview

WebSpherical CNNs. In International Conference on Learning Representations (ICLR), 2024. Taco S Cohen, Mario Geiger, and Maurice Weiler. Intertwiners between induced representations (with applications to the theory of equivariant neural networks). arXiv preprint arXiv:1803.10743, 2024. Taco S Cohen and Max Welling. burkholder golf carts in pa

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WebSuch a high computational cost often prohibits the use of strictly equivariant spherical CNNs. We develop two new strictly equivariant layers with reduced complexity OpCL4q and OpCL3 log Lq, making larger, more expressive models computationally feasible. ... Efficient Generalized Spherical CNNs. In: Proceedings of ICLR 2024 ... WebSep 27, 2024 · No existing spherical convolutional neural network (CNN) framework is both computationally scalable and rotationally equivariant. Continuous approaches capture rotational equivariance but are often prohibitively computationally demanding. Discrete approaches offer more favorable computational performance but at the cost of … WebApr 12, 2024 · Abstract Precipitation is a vital process in the water cycle. Accurate estimation of the precipitation rate underpins the success of hydrological simulations, flood predictions, and water resource management. Satellite infrared (IR) data, with high temporal resolution and wide coverages, have been commonly used in precipitation inversion. … halo hands rocky mount