title: Scaling Spherical CNNs
summary: Spherical CNNs generalize CNNs to functions on the sphere, by using spherical
  convolutions as the main linear operation. The most accurate and efficient way
  to compute spherical convolutions is in the spectral domain (via the
  convolution theorem), which is still costlier than the usual planar
  convolutions. For this reason, applications of spherical CNNs have so far been
  limited to small problems that can be approached with low model capacity. In
  this work, we show how spherical CNNs can be scaled for much larger problems.
  To achieve this, we make critical improvements including novel variants of
  common model components, an implementation of core operations to exploit
  hardware accelerator characteristics, and application-specific input
  representations that exploit the properties of our model. Experiments show our
  larger spherical CNNs reach state-of-the-art on several targets of the QM9
  molecular benchmark, which was previously dominated by equivariant graph neural
  networks, and achieve competitive performance on multiple weather forecasting
  tasks. Our code is available at
  https://github.com/google-research/spherical-cnn.
paper_authors._id: 6482c190120600dcbe602419
paper_authors.name: Carlos Esteves
paper_authors._id: 6482c190120600dcbe60241a
paper_authors.name: Jean-Jacques Slotine
paper_authors._id: 6482c190120600dcbe60241b
paper_authors.name: Ameesh Makadia
paper_title: Scaling Spherical CNNs
paper_summary: Spherical CNNs generalize CNNs to functions on the sphere, by using spherical
  convolutions as the main linear operation. The most accurate and efficient way
  to compute spherical convolutions is in the spectral domain (via the
  convolution theorem), which is still costlier than the usual planar
  convolutions. For this reason, applications of spherical CNNs have so far been
  limited to small problems that can be approached with low model capacity. In
  this work, we show how spherical CNNs can be scaled for much larger problems.
  To achieve this, we make critical improvements including novel variants of
  common model components, an implementation of core operations to exploit
  hardware accelerator characteristics, and application-specific input
  representations that exploit the properties of our model. Experiments show our
  larger spherical CNNs reach state-of-the-art on several targets of the QM9
  molecular benchmark, which was previously dominated by equivariant graph neural
  networks, and achieve competitive performance on multiple weather forecasting
  tasks. Our code is available at
  https://github.com/google-research/spherical-cnn.
paper_ai_summary: Scaling spherical CNNs achieves state-of-the-art performance in molecular benchmarks and competitive performance in weather forecasting by leveraging hardware accelerators and novel model components.
paper_ai_keywords: spherical CNNs spherical convolutions spectral domain convolution theorem hardware accelerators equivariant graph neural networks QM9 weather forecasting