@inproceedings{tibau2018supernovae,
    type = {inproceedings},
    key = {tibau2018supernovae},
    title = {SupernoVAE: VAE based Kernel-PCA for Analysis of Spatio-Temporal Earth Data},
    author = {Xavier-Andoni Tibau and Christian Requena-Mesa and Christian Reimers and Joachim Denzler and Veronika Eyring and Markus Reichstein and Jakob Runge},
    booktitle = {International Workshop on Climate Informatics (CI)},
    year = {2018},
    pages = {73-76},
    abstract = {It is a constant challenge to better understand the underlying dynamics and forces driving the Earth system. Advances in the field of deep learning allow for unprecedented results, but use of these methods in Earth system science is still very limited. We present a framework that makes use of a convolutional variational autoencoder as a learnable kernel from which to extract spatio-temporal dynamics via PCA. The method promises the ability of deep learning to digest highly complex spatio-temporal datasets while allowing expert interpretability. Preliminary results over two artificial datasets, with chaotic and stochastic temporal dynamics, show that the method can recover a latent driver parameter while baseline approaches cannot. While further testing on the limitations of the method is needed and experiments on real Earth datasets are in order, the present approach may contribute to further the understanding of Earth datasets that are highly non-linear.},
    doi = {10.5065/D6BZ64XQ},
    url = {https://opensky.ucar.edu/islandora/object/technotes%3A571/datastream/PDF/view},
}