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Readme upd #16

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13 changes: 10 additions & 3 deletions README.md
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Expand Up @@ -9,9 +9,16 @@
`Probaforms` is a python library of conditional Generative Adversarial Networks, Normalizing Flows, Variational Autoencoders and other generative models for tabular data. All models have a sklearn-like interface to enable rapid use in a variety of science and engineering applications.

## Implemented conditional models
- Variational Autoencoder (CVAE)
- Wasserstein GAN (WGAN)
- Real NVP

[//]: # (Use Vancouver reference style below)

| **Model** | **Type** | **Paper** |
|:-----------------:|:----------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ConditionalNormal | MDN | Bishop CM. [Mixture density networks](https://publications.aston.ac.uk/id/eprint/373/1/NCRG_94_004.pdf). 1994. |
| CVAE | VAE | Kingma DP, Welling M. [Auto-encoding variational bayes](https://openreview.net/forum?id=33X9fd2-9FyZd). [arXiv:1312.6114](https://arxiv.org/abs/1312.6114). ICLR 2014. |
| ConditionalWGAN | GAN | Arjovsky M, Chintala S, Bottou L. [Wasserstein generative adversarial networks](https://proceedings.mlr.press/v70/arjovsky17a.html). [arXiv:1701.07875](https://arxiv.org/abs/1701.07875). ICML 2017. |
| RealNVP | Normalizing Flow | Dinh L, Sohl-Dickstein J, Bengio S. [Density estimation using real nvp](https://openreview.net/forum?id=HkpbnH9lx). [arXiv:1605.08803](https://arxiv.org/abs/1605.08803). ICLR 2017. |


## Installation
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