Update README.md

README.md

@@ -1,3 +1,3 @@
 # Robust Meta-learning for Mixed Linear Regression with Small Batches
 
-This project contains the code for the paper accepted at NeurIPS 2020 with the above title. The file `RPCA.py` contains an implementation of the algorithm and the simulations done in the paper. This project also contains the code of its preceeding [ICML 2020 paper](https://arxiv.org/abs/2002.08936) where we provide the base code for an implementation of our end-to-end meta learning algorithm for a mixture of linear regression. As a special case, we simulate this algorithm on a mixture of sine wave reconstruction problem which is a generalization of linear regression under orthonormal polynomial feturization.
+This project contains the code for the paper accepted at NeurIPS 2020 with the above title. The file `RPCA.py` contains an implementation of the algorithm and the simulations done in the paper. This project also contains the code of its preceeding [ICML 2020 paper](https://arxiv.org/abs/2002.08936) where we provide the base code for an implementation of our end-to-end meta learning algorithm for a mixture of linear regression in the file `meta_learning_utils.py`. As a special case, in the file `meta_learning_sine.py` we simulate this algorithm on a mixture of sine wave reconstruction problem which is a generalization of linear regression under orthonormal polynomial feturization.