  Coordinate networks are widely used in computer vision due to their ability
to represent signals as compressed, continuous entities. However, training
these networks with first-order optimizers can be slow, hindering their use in
real-time applications. Recent works have opted for shallow voxel-based
representations to achieve faster training, but this sacrifices memory
efficiency. This work proposes a solution that leverages second-order
optimization methods to significantly reduce training times for coordinate
networks while maintaining their compressibility. Experiments demonstrate the
effectiveness of this approach on various signal modalities, such as audio,
images, videos, shape reconstruction, and neural radiance fields.
