Add references, expand README.md (#117)

* Add references, expand README.md

README.md

@@ -1,19 +1,45 @@
-## Hivemind
+## hivemind: decentralized deep learning in PyTorch
 [![Build status](https://circleci.com/gh/learning-at-home/hivemind.svg?style=shield)](https://circleci.com/gh/learning-at-home/hivemind)
 [![Documentation Status](https://readthedocs.org/projects/learning-at-home/badge/?version=latest)](https://learning-at-home.readthedocs.io/en/latest/?badge=latest)
 [![Gitter](https://badges.gitter.im/learning-at-home/hivemind.svg)](https://gitter.im/learning-at-home/hivemind?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
 
-A library to train large neural networks across the internet. Imagine training one huge transformer
-  on thousands of computers from universities, companies, and volunteers.
+Hivemind is a PyTorch library to train large neural networks across the Internet. Imagine training one huge Transformer model
+  on thousands of computers from different universities, companies, and volunteers.
 
 ![img](https://i.imgur.com/GPxolxb.gif)
 
-##### Links:
- * What is hivemind all about? Here's a [3-minute read](https://learning-at-home.github.io) or a [full paper](https://arxiv.org/abs/2002.04013)
- * [Quickstart tutorial](https://learning-at-home.readthedocs.io/en/latest/user/quickstart.html) - install hivemind, 
+### Key Features
+ * Train neural networks of arbitrary size: parts of their layers are distributed across the participants
+ * Run distributed training without master node: Distributed Hash Table allows to connect computers in a decentralized network
+ * Fault-tolerant backpropagation: forward and backward passes succeed even if some nodes are unresponsive or take too long to respond
+
+To learn more about the idea behind this library and its components, see https://learning-at-home.github.io or read the [NeurIPS 2020 paper](https://arxiv.org/abs/2002.04013)
+
+### Documentation
+ * [Quickstart tutorial](https://learning-at-home.readthedocs.io/en/latest/user/quickstart.html): install hivemind, 
     set up a server and train experts  
  * Documentation & guides: [learning-at-home.readthedocs.io](https://learning-at-home.readthedocs.io)
- * Want to contribute? take a look at the issues or join [our chat room](https://gitter.im/learning-at-home/hivemind)
- * [Developer's guide](https://learning-at-home.readthedocs.io/en/latest/user/contributing.html): best practices, tests and performance benchmarks
- * [Related](https://learning-at-home.readthedocs.io/en/latest/user/acknowledgements.html) projects and acknowledgements
- 
+
+### Contributing
+Hivemind is currently at the active development stage, and we welcome all contributions from bug fixes and documentation improvements to entirely new features. 
+If you want to contribute to hivemind, take a look at the issues or join [our chat room](https://gitter.im/learning-at-home/hivemind).
+The [Developer's guide](https://learning-at-home.readthedocs.io/en/latest/user/contributing.html) page contains best practices, as well as description of tests and performance benchmarks.
+
+### References
+You can read the paper that inspired hivemind here:
+
+[Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts](https://arxiv.org/abs/2002.04013) (Max Ryabinin and Anton Gusev, NeurIPS 2020).
+```
+@misc{ryabinin2020crowdsourced,
+      title={Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts}, 
+      author={Max Ryabinin and Anton Gusev},
+      year={2020},
+      eprint={2002.04013},
+      archivePrefix={arXiv},
+      primaryClass={cs.DC}
+}
+```
+The initial implementation of hivemind used to conduct experiments for the paper is available here: [mryab/learning-at-home](https://github.com/mryab/learning-at-home).
+
+In the docs, we list several [related](https://learning-at-home.readthedocs.io/en/latest/user/acknowledgements.html) projects and acknowledgements.
+