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diff --git a/README.md b/README.md
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 # LoRA-Ensemble: Uncertainty Modelling for Self-attention Networks
 Michelle Halbheer, Dominik J. Mühlematter, Alexander Becker, Dominik Narnhofer, Helge Aasen, Konrad Schindler and Mehmet Ozgur Turkoglu - 2024
 
-[[Paper on ArXiv]](https://arxiv.org/abs/2405.14438)
-
 ## Pre-trained models
 This repository contains the pre-trained models corresponding to the code we released on [GitHub](https://github.com/prs-eth/LoRA-Ensemble/).
 The usage of the models with our pipeline is described in the GitHub repository.
 This repository only contains the models for our final experiments on the CIFAR-10, CIFAR-100 and HAM10000 datasets, not, however, for all intermediate results.
 The results of the ESC-50 dataset cannot be published at this time, as it would require storing five models per epoch during training in order to store all models of the five-fold cross-validation. This is infeasible on our infrastructure so we only release models for CIFAR-10, CIFAR-100 and HAM10000.
 
-## Base models
-Alongside our pre-trained models we release the base models we used for our models. This is to ensure the reproducibility of our results even if the weights by `torchvision` and `timm` should get changed.
-
 ## Citation
 If you find our work useful or interesting or use our code, please cite our paper as follows
 ```latex
@@ -23,7 +18,7 @@ If you find our work useful or interesting or use our code, please cite our pape
   title = {LoRA-Ensemble: Uncertainty Modelling for Self-attention Networks},
   author = {Halbheer, Michelle and M\"uhlematter, Dominik Jan and Becker, Alexander and Narnhofer, Dominik and Aasen, Helge and Schindler, Konrad and Turkoglu, Mehmet Ozgur}
   year = {2024}
-  note = {arXiv: 2405.14438}
+  note = {arXiv: <arxiv code>}
 }
 ```
 
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