Update README.md

Author: ranganathkrishnan

README.md

@@ -39,7 +39,7 @@ Please refer to [documentation](doc/bayesian_torch.layers.md#layers) of Bayesian
 
 Other features include:
 - [x] [AvUC](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/utils/avuc_loss.py): Accuracy versus Uncertainty Calibration loss [[Krishnan and Tickoo 2020](https://proceedings.neurips.cc/paper/2020/file/d3d9446802a44259755d38e6d163e820-Paper.pdf)]
-- [x] [MOPED](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/utils/util.py#L72): specifying weight priors and variational posteriors with Empirical Bayes [[Krishnan et al. 2019](https://arxiv.org/abs/1906.05323)]
+- [x] [MOPED](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/utils/util.py#L72): Specifying weight priors and variational posteriors with Empirical Bayes [[Krishnan et al. 2020](https://ojs.aaai.org/index.php/AAAI/article/view/5875)]
 - [x] [dnn_to_bnn](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/models/dnn_to_bnn.py#L127): An API to convert deterministic deep neural network (dnn) model of any architecture to Bayesian deep neural network (bnn) model, simplifying the model definition i.e. drop-in replacements  of Convolutional, Linear and LSTM layers to corresponding Bayesian layers. This will enable seamless conversion of existing topology of larger models to Bayesian deep neural network models for extending towards uncertainty-aware applications. 
 
 ## Installation