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
Nazari, P., Damrich, S., Hamprecht, F.A.
Geometric Autoencoders – What You See is What You Decode
03
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
04
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
05
Sun, S., Woerner, S., Maier, A., Koch, L.M., Baumgartner,, C.F.
Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals
06
Grote, T., & Berens, P
Uncertainty, evidence, and the integration of machine learning into medical practice
07
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
08
Franke, K., Cai, C., Ponder, K., Fu, J., Sokoloski, S., Berens, P., & Tolias, A. S.
Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky.
09
Böhm, J. N., Berens, P., & Kobak, D.
Unsupervised visualization of image datasets using contrastive learning
10
Damrich, S., Böhm, J. N., Hamprecht, F. A., & Kobak, D.
From t-SNE to UMAP with contrastive learning
11
Bachmann, F., Hennig, P., Kobak, D.
Wasserstein tSNE
12
Janschewski, J., Käppler, C., & Berens, P.