In the Department of Data Science we generate knowledge from data to advance neuroscience and ophthalmology.
01
Sun, S., Koch, L. M., & Baumgartner, C. F.
Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?
02
Ayhan, M. S., Faber, H., Kühlewein, L., Inhoffen, W., Aliyeva, G., Ziemssen, F., & Berens, P.
Multitask Learning for Activity Detection in Neovascular Age-Related Macular Degeneration
03
Djoumessi, K. R. D., Ilanchezian, I., Kühlewein, L., Faber, H., Baumgartner, C. F., Bah, B., Berens, P. & Koch, L. M.
Sparse Activations for Interpretable Disease Grading
04
Sun, S., Woerner, S., Maier, A., Koch, L.M., Baumgartner,, C.F.
Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals
05
Grote, T., & Berens, P
Uncertainty, evidence, and the integration of machine learning into medical practice
06
Congiu, M., Mondoloni, S., Zouridis, I. S., Schmors, L., Lecca, S., Lalive, A. L., Ginggen, K., Deng, F., Berens, P., Paolicelli, R. C., Li, Y., Burgalossi, A. & Mameli, M.
Plasticity of neuronal dynamics in the lateral habenula for cue-punishment associative learning
07
Böhm, J. N., Berens, P., & Kobak, D.
Unsupervised visualization of image datasets using contrastive learning
08
Damrich, S., Böhm, J. N., Hamprecht, F. A., & Kobak, D.
From t-SNE to UMAP with contrastive learning
09
Bachmann, F., Hennig, P., Kobak, D.
Wasserstein tSNE
10
Janschewski, J., Käppler, C., & Berens, P.