Add acknowledgements

README.md

@@ -24,10 +24,9 @@ The package supports:
 
 The slides of the talk are available as [Pluto notebook](https://talks.widmann.dev/2021/07/calibration/).
 
-## References
+## Citing
 
-If you use CalibrationAnalysis.jl as part of your research, teaching, or other activities,
-please consider citing the following publications:
+If you use CalibrationAnalysis.jl as part of your research, teaching, or other activities, please consider citing the following publications:
 
 Widmann, D., Lindsten, F., & Zachariah, D. (2019). [Calibration tests in multi-class
 classification: A unifying framework](https://proceedings.neurips.cc/paper/2019/hash/1c336b8080f82bcc2cd2499b4c57261d-Abstract.html). In
@@ -36,3 +35,7 @@ classification: A unifying framework](https://proceedings.neurips.cc/paper/2019/
 Widmann, D., Lindsten, F., & Zachariah, D. (2021).
 [Calibration tests beyond classification](https://openreview.net/forum?id=-bxf89v3Nx).
 *International Conference on Learning Representations (ICLR 2021)*.
+
+## Acknowledgements
+
+This work was financially supported by the Swedish Research Council via the projects *Learning of Large-Scale Probabilistic Dynamical Models* (contract number: 2016-04278), *Counterfactual Prediction Methods for Heterogeneous Populations* (contract number: 2018-05040), and *Handling Uncertainty in Machine Learning Systems* (contract number: 2020-04122), by the Swedish Foundation for Strategic Research via the project *Probabilistic Modeling and Inference for Machine Learning* (contract number: ICA16-0015), by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and by ELLIIT.