Update README.md

Author: ranganathkrishnan

README.md

@@ -45,8 +45,8 @@ Bayesian-Torch is designed to be flexible and enables seamless extension of dete
 
 **Key features:**
 * [dnn_to_bnn()](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/models/dnn_to_bnn.py#L127): Seamless conversion of model to be Uncertainty-aware. 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. 
-* [MOPED](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/utils/util.py#L72): Specifying weight priors and variational posteriors in Bayesian neural networks with Empirical Bayes [[Krishnan et al. 2020](https://ojs.aaai.org/index.php/AAAI/article/view/5875)]
-* [Quantization](https://github.com/IntelLabs/bayesian-torch/tree/main/bayesian_torch/ao): Post Training Quantization of Bayesian deep neural network models with simple API's [enable_prepare()](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/ao/quantization/quantize.py#L134) and [convert()](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/ao/quantization/quantize.py#L160)
+* [MOPED](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/utils/util.py#L72): Scale Bayesian inference to large scale models by specifying weight priors and variational posteriors in Bayesian neural networks with Empirical Bayes [[Krishnan et al. 2020](https://ojs.aaai.org/index.php/AAAI/article/view/5875)]
+* [Quantization](https://github.com/IntelLabs/bayesian-torch/tree/main/bayesian_torch/ao): Post Training Quantization of Bayesian deep neural network models for INT8 inference with simple API's [enable_prepare()](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/ao/quantization/quantize.py#L134) and [convert()](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/ao/quantization/quantize.py#L160)
 * [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)]
 
 ## Installing Bayesian-Torch