Geometric Deep Learning on Groups
Continuous vs discrete approaches on the sphere —  Ideally geometric deep learning techniques on groups would encode equivariance to group transformations, to provide well-behaved representation spaces and excellent performance, while also being computationally efficient. However, no single approach provides both of these desirable properties. Continuous approaches offer excellent equivariance but with a very large computational cost. Discrete approaches…
Geometric Deep Learning on Groups Continuous vs discrete approaches on the sphere —  Ideally geometric deep learning techniques on groups would encode equivariance to group transformations, to provide well-behaved representation spaces and excellent performance, while also being computationally efficient. However, no single approach provides both of these desirable properties. Continuous approaches offer excellent equivariance but with a very large computational cost. Discrete approaches… deep-learning
Geometric Deep Learning Groups Continuous v discrete approach sphere — Ideally geometric deep learning technique group would encode equivariance group transformation provide wellbehaved representation space excellent performance also computationally efficient However single approach provides desirable property Continuous approach offer excellent equivariance large computational cost Discrete approaches… deeplearning