Publications

A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections
Xavier-Andoni Tibau and Christian Reimers and Andreas Gerhardus and Joachim Denzler and Veronika Eyring and Jakob Runge.
Environmental Data Science. 1: pages E12. 2022.
[bibtex] [web] [abstract]

Investigating Neural Network Training on a Feature Level using Conditional Independence
Niklas Penzel and Christian Reimers and Paul Bodesheim and Joachim Denzler.
ECCV Workshop on Causality in Vision (ECCV-WS). Pages 383-399. 2022.
[bibtex] [pdf] [abstract]

Conditional Dependence Tests Reveal the Usage of ABCD Rule Features and Bias Variables in Automatic Skin Lesion Classification
Christian Reimers and Niklas Penzel and Paul Bodesheim and Jakob Runge and Joachim Denzler.
CVPR ISIC Skin Image Analysis Workshop (CVPR-WS). Pages 1810-1819. 2021.
[bibtex] [pdf] [web] [abstract]

Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data
Christian Reimers and Paul Bodesheim and Jakob Runge and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 48-62. 2021.
[bibtex] [pdf] [abstract]

Investigating the Consistency of Uncertainty Sampling in Deep Active Learning
Niklas Penzel and Christian Reimers and Clemens-Alexander Brust and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 159-173. 2021.
[bibtex] [pdf] [web] [abstract]

Determining the Relevance of Features for Deep Neural Networks
Christian Reimers and Jakob Runge and Joachim Denzler.
European Conference on Computer Vision. Pages 330-346. 2020.
[bibtex] [abstract]

Deep Learning--an Opportunity and a Challenge for Geo-and Astrophysics
Christian Reimers and Christian Requena-Mesa. 2020.
[bibtex]

Using Causal Inference to Globally Understand Black Box Predictors Beyond Saliency Maps
Christian Reimers and Jakob Runge and Joachim Denzler.
International Workshop on Climate Informatics (CI). 2019.
[bibtex] [pdf] [abstract]

Toy models to analyze emergent constraint approaches
Xavier-Andoni Tibau and Christian Reimers and Veronika Eyring and Joachim Denzler and Markus Reichstein and Jakob Runge.
European Geosciences Union General Assembly (EGU): Abstract + Poster Presentation. 2019.
[bibtex] [pdf] [web] [abstract]

Abstract: Climate projections are limited by the arising uncertainties associated with not well-known physical processes in climate change. In every new generation, climate models improve several aspects of projections, while others remain in the same uncertainty range, especially those regarding equilibrium climate sensitivity (ECS) and climate feedbacks. Emergent constraints, defined as a 'physically explainable empirical relationships between characteristics of the current climate and long-term climate prediction that emerge in collections of climate model simulations' [1], is a promising novel approach that can shed light on climate change uncertainties and improve climate models. Since the first emergent constraint was proposed to constrain the Surface Albedo Feedback in 2006 by Hall & Qu [2], several of them have been proposed for constraining feedbacks and uncertainties of climate models, e.g., ECS, low-level cloud optical depth or tropical primary production. Emergent constraints have already been a prolific approach to improve our climate models. The typical approach to identify emergent constraints comes from expert knowledge when this is used to explore climate data and select those emergent constraints that are physically explainable. Caldwell et al. [3] presented a work where a new approach was suggested. In that paper, they attempt to identify quantities in the current climate which are skillful predictors of ECS yet can be constrained by observations. One of the main conclusions of this work was that the development of data mining methods for identifying emergent constraints should be aware of spurious emergent relations that could arise by chance. This becomes especially relevant in the next phase of the CMIP Project (6th). In the present work, we discuss simple spatiotemporal climate ("toy") models to analyze and evaluate methodologies to identify predictors for emergent constraints. Such models are simple enough to be analyzed not only empirically but also analytically, and at the same time incorporate relevant aspects of the complexity of a nonlinear dynamical spatiotemporal system. Consequently, they can be used to study assumptions and pitfalls of data mining methods for emergent constraints and guide the development of future approaches. [1]: Klein, S. A., & Hall, A. (2015). Emergent constraints for cloud feedbacks. Current Climate Change Reports, 1(4), 276-287. [2]: Hall, A., & Qu, X. (2006). Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophysical Research Letters, 33(3). [3]: Caldwell, P. M., Bretherton, C. S., Zelinka, M. D., Klein, S. A., Santer, B. D., & Sanderson, B. M. (2014). Statistical significance of climate sensitivity predictors obtained by data mining. Geophysical Research Letters, 41(5), 1803-1808.

SupernoVAE: Using deep learning to find spatio-temporal dynamics in Earth system data
Xavier-Andoni Tibau and Christian Requena-Mesa and Christian Reimers and Joachim Denzler and Veronika Eyring and Markus Reichstein and Jakob Runge.
American Geophysical Union Fall Meeting (AGU): Abstract + Poster Presentation. 2018.
[bibtex] [web] [abstract]

Abstract: Exploring and understanding spatio-temporal patterns on Earth system datasets is one of the principal goals of the climate and geo-science communities. In this direction, Empirical Orthogonal Functions (EOFs) have been used to characterize phenomena such as the El Nino Southern Oscillation, the Arctic jet stream or the Indian Monsoon. However, EOF analysis has several limitations, for example, it can only identify linear and orthogonal patterns. We present a framework that makes use of a convolutional variational autoencoder (VAE) as a learnable feature function to extract spatio-temporal dynamics via PCA. The VAE encodes the information in an abstract space of higher order features representing different patterns. Over this space, PCA is performed to obtain a spatial representation of related temporal dynamics. We have used three datasets, two artificial datasets where the dynamics are ruled by a hidden spatially varying parameter and an observational reanalysis dataset of monthly sea surface temperature from 1898 to 2014. The artificial datasets have chaotic and, chaotic and stochastic dynamics depending on the spatial hidden parameter. As baseline methods, EOF analysis and Kernel PCA were performed over the original spaces. For the two artificial datasets, we found a high correlation between some of the first Principal Components on the feature space and the spatial hidden parameter. This correlation was not found using baseline methods in the original space. In the reanalysis dataset, the method was able to find known modes, such as ENSO, as well as other patterns that baseline methods did not reveal that might have inmmediate effect on how we understand the earth system after expert interpretation. These results provide a proof of concept: SupernoVAE is not only able to extract well-known climate patterns previously characterized with linear EOF analysis, but also allows to extract non-linear and non-orthogonal patterns that can help in analyzing Earth system dynamics that could not be characterized before.

SupernoVAE: VAE based Kernel-PCA for Analysis of Spatio-Temporal Earth Data
Xavier-Andoni Tibau and Christian Requena-Mesa and Christian Reimers and Joachim Denzler and Veronika Eyring and Markus Reichstein and Jakob Runge.
International Workshop on Climate Informatics (CI). Pages 73-76. 2018.
[bibtex] [web] [abstract]