@InProceedings{dangel2020modular,
  title =        {Modular Block-diagonal Curvature Approximations for
                  Feedforward Architectures},
  author =       {Dangel, Felix and Harmeling, Stefan and Hennig, Philipp},
  booktitle =    {Proceedings of the Twenty Third International Conference on
                  Artificial Intelligence and Statistics},
  pages =        {799--808},
  year =         2020,
  editor =       {Chiappa, Silvia and Calandra, Roberto},
  volume =       108,
  series =       {Proceedings of Machine Learning Research},
  address =      {Online},
  month =        {26--28 Aug},
  publisher =    {PMLR},
  pdf =          {http://proceedings.mlr.press/v108/dangel20a/dangel20a.pdf},
  url =          {http://proceedings.mlr.press/v108/dangel20a.html},
  abstract =     {We propose a modular extension of backpropagation for the
                  computation of block-diagonal approximations to various
                  curvature matrices of the training objective (in particular,
                  the Hessian, generalized Gauss-Newton, and positive-curvature
                  Hessian). The approach reduces the otherwise tedious manual
                  derivation of these matrices into local modules, and is easy
                  to integrate into existing machine learning libraries.
                  Moreover, we develop a compact notation derived from matrix
                  differential calculus. We outline different strategies
                  applicable to our method. They subsume recently-proposed
                  block-diagonal approximations as special cases, and are
                  extended to convolutional neural networks in this work.}
}