Add link to paper (#1)

- Add link to paper (55002eff8fa94ec0ab1ad2f0a8af40cd933c9680)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -168,7 +168,7 @@ tags:
 - pytorch
 ---
 # Introduction
-[[`AIMv2 Paper`](#)] [[`BibTeX`](#citation)]
+[[`AIMv2 Paper`](https://arxiv.org/abs/2411.14402)] [[`BibTeX`](#citation)]
 
 We introduce the AIMv2 family of vision models pre-trained with a multimodal autoregressive objective.
 AIMv2 pre-training is simple and straightforward to train and scale effectively. Some AIMv2 highlights include:
@@ -226,12 +226,14 @@ outputs = model(**inputs)
 ## Citation
 If you find our work useful, please consider citing us as:
 ```bibtex
-@misc{fini2024multimodal,
-  title         = {Multimodal Autoregressive Pre-training of Large Vision Encoders},
-  author        = {Enrico Fini and Mustafa Shukor and Xiujun Li and Philipp Dufter and Michal Klein and David Haldimann and Sai Aitharaju and Victor Guilherme Turrisi da Costa and Louis Béthune and Zhe Gan and Alexander T Toshev and Marcin Eichner and Moin Nabi and Yinfei Yang and Joshua M. Susskind and Alaaeldin El-Nouby},
-  year          = {2024},
-  archivePrefix = {arXiv},
-  primaryClass  = {cs.CV},
+@misc{fini2024multimodalautoregressivepretraininglarge,
+      title={Multimodal Autoregressive Pre-training of Large Vision Encoders}, 
+      author={Enrico Fini and Mustafa Shukor and Xiujun Li and Philipp Dufter and Michal Klein and David Haldimann and Sai Aitharaju and Victor Guilherme Turrisi da Costa and Louis Béthune and Zhe Gan and Alexander T Toshev and Marcin Eichner and Moin Nabi and Yinfei Yang and Joshua M. Susskind and Alaaeldin El-Nouby},
+      year={2024},
+      eprint={2411.14402},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2411.14402}, 
 }
 ```