Update README.md

Author: Christos Psarras

README.md

@@ -5,16 +5,33 @@ that assess the extent to which said languages translate high-level linear algeb
 ## Setup
 For instructions on how to setup the benchmarks and install all languages used (and linking to Intel MKL), please consult the [Wiki](https://github.com/ChrisPsa/LAMP_benchmark/wiki) page.
 
-## Citation
-More details can be found in our [paper](https://arxiv.org/abs/1911.09421):
+## Related Publications
+* [The Linear Algebra Mapping Problem. Current state of linear algebra languages and libraries](https://arxiv.org/abs/1911.09421):
 
+```bibtex
+@misc{10.48550/arxiv.1911.09421,
+    title        = {The Linear Algebra Mapping Problem. Current state of linear algebra languages and libraries},
+    author       = {Psarras, Christos and Barthels, Henrik and Bientinesi, Paolo},
+    year         = 2019,
+    publisher    = {arXiv},
+    doi          = {10.48550/ARXIV.1911.09421},
+    url          = {https://arxiv.org/abs/1911.09421},
+    copyright    = {arXiv.org perpetual, non-exclusive license},
+    keywords     = {Mathematical Software (cs.MS), Programming Languages (cs.PL), FOS: Computer and information sciences, FOS: Computer and information sciences}
+}
 ```
-@misc{psarras2019lamp,
-      title={The Linear Algebra Mapping Problem},
-      author={Christos Psarras and Henrik Barthels and Paolo Bientinesi},
-      year={2019},
-      eprint={1911.09421},
-      archivePrefix={arXiv},
-      primaryClass={cs.MS}
+
+* [Benchmarking the Linear Algebra Awareness of TensorFlow and PyTorch](https://arxiv.org/abs/2202.09888)
+
+```bibtex
+@misc{10.48550/arxiv.2202.09888,
+	title        = {Benchmarking the Linear Algebra Awareness of TensorFlow and PyTorch},
+	author       = {Sankaran, Aravind and Alashti, Navid Akbari and Psarras, Christos and Bientinesi, Paolo},
+	year         = 2022,
+	publisher    = {arXiv},
+	doi          = {10.48550/ARXIV.2202.09888},
+	url          = {https://arxiv.org/abs/2202.09888},
+	copyright    = {Creative Commons Attribution 4.0 International},
+	keywords     = {Mathematical Software (cs.MS), Machine Learning (cs.LG), Performance (cs.PF), FOS: Computer and information sciences, FOS: Computer and information sciences}
 }
 ```