-- Added and updated Readme

Author: manujosephv

README.md

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-![PyTorch Tabular](docs/imgs/pytorch_tabular_logo.png)
+![PyTorch Tabular](docs/imgs/pytorch_tabular_logo.png)    
+
 [![pypi](https://img.shields.io/pypi/v/pytorch_tabular.svg)](https://pypi.python.org/pypi/pytorch_tabular)
 [![travis](https://img.shields.io/travis/manujosephv/pytorch_tabular.svg)](https://travis-ci.com/manujosephv/pytorch_tabular)
 [![documentation status](https://readthedocs.org/projects/pytorch_tabular/badge/?version=latest)](https://pytorch_tabular.readthedocs.io/en/latest/?badge=latest)
@@ -62,9 +63,11 @@ For complete Documentation with tutorials visit []
 
 ## Available Models
 
-* FeedForward Network with Category Embedding is a simple FF network, but with and Embedding layers for the categorical columns.
+* FeedForward Network with Category Embedding is a simple FF network, but with an Embedding layers for the categorical columns.
 * [Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data](https://arxiv.org/abs/1909.06312) is a model presented in ICLR 2020 and according to the authors have beaten well-tuned Gradient Boosting models on many datasets.
 * [TabNet: Attentive Interpretable Tabular Learning](https://arxiv.org/abs/1908.07442) is another model coming out of Google Research which uses Sparse Attention in multiple steps of decision making to model the output.
+* [Mixture Density Networks](https://publications.aston.ac.uk/id/eprint/373/1/NCRG_94_004.pdf) is a regression model which uses gaussian components to approximate the target function and  provide a probabilistic prediction out of the box.
+* [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921) is a model which tries to learn interactions between the features in an automated way and create a better representation and then use this representation in downstream task
 
 To implement new models, see the [How to implement new models tutorial](https://github.com/manujosephv/pytorch_tabular/blob/main/docs/04-Implementing%20New%20Architectures.ipynb). It covers basic as well as advanced architectures.