Update README.md

Author: ranganathkrishnan

README.md

@@ -27,21 +27,20 @@ The repository has implementations for the following Bayesian layers:
       LinearRadial
       Conv1dRadial, Conv2dRadial, Conv3dRadial, ConvTranspose1dRadial, ConvTranspose2dRadial, ConvTranspose3dRadial
       LSTMRadial
--->
 
 - [ ] **Variational layers with Gaussian mixture model (GMM) posteriors using reparameterized Monte Carlo estimators** (in `pre-alpha`)
 
       LinearMixture
       Conv1dMixture, Conv2dMixture, Conv3dMixture, ConvTranspose1dMixture, ConvTranspose2dMixture, ConvTranspose3dMixture
       LSTMMixture
+-->
 
 Please refer to [documentation](doc/bayesian_torch.layers.md#layers) of Bayesian layers for details.
 
 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)]
-- [ ] dnn_to_bnn: An API to convert deterministic deep neural network (dnn) model of any architecture to Bayesian deep neural network (bnn) model, simplifying the model definition by replacing neural network layers with corresponding Bayesian layers (`updating soon...`)
- 
+- [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
 
@@ -119,19 +118,44 @@ sh scripts/test_deterministic_cifar.sh
 If you use this code, please cite as:
 ```sh
 @misc{krishnan2020bayesiantorch,
-    author = {Ranganath Krishnan and Piero Esposito},
+    author = {Ranganath Krishnan and Piero Esposito and Mahesh Subedar},
     title = {Bayesian-Torch: Bayesian neural network layers for uncertainty estimation},
     year = {2020},
     publisher = {GitHub},
     howpublished = {\url{https://github.com/IntelLabs/bayesian-torch}}
 }
 ```
-
-Cite the weight sampling methods as well: [Blundell et al. 2015](https://arxiv.org/abs/1505.05424); [Wen et al. 2018](https://arxiv.org/abs/1803.04386)
+Accuracy versus Uncertainty Calibration (AvUC) loss
+```sh
+@inproceedings{NEURIPS2020_d3d94468,
+ title = {Improving model calibration with accuracy versus uncertainty optimization},
+ author = {Krishnan, Ranganath and Tickoo, Omesh},
+ booktitle = {Advances in Neural Information Processing Systems},
+ volume = {33},
+ pages = {18237--18248},
+ year = {2020},
+ url = {https://proceedings.neurips.cc/paper/2020/file/d3d9446802a44259755d38e6d163e820-Paper.pdf}
+ 
+}
+```
+MOdel Priors with Empirical Bayes using DNN (MOPED)
+```sh
+@inproceedings{krishnan2020specifying,
+  title={Specifying weight priors in bayesian deep neural networks with empirical bayes},
+  author={Krishnan, Ranganath and Subedar, Mahesh and Tickoo, Omesh},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={34},
+  number={04},
+  pages={4477--4484},
+  year={2020},
+  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5875}
+}
+```
 
 **Contributors**
 - Ranganath Krishnan 
 - Piero Esposito 
+- Mahesh Subedar
 
 This code is intended for researchers and developers, enables to quantify principled uncertainty estimates from deep neural network predictions using stochastic variational inference in Bayesian neural networks. 
 Feedbacks, issues and contributions are welcome. Email to <[email protected]> for any questions.