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Fix small typos in README.md #2

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6 changes: 3 additions & 3 deletions README.md
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Expand Up @@ -13,7 +13,7 @@ This is the official repository for the paper [SE(3)-Stochastic Flow Matching fo
We propose a new family of [Flow Matching](https://openreview.net/forum?id=PqvMRDCJT9t) methods called FoldFlow tailored for distributions on SE(3) and with a focus on protein backbone generation. Our 3 proposed methods are:

- The first one is **FoldFlow-base**. Inspired by [Riemannian Flow Matching](https://arxiv.org/abs/2302.03660), we develop a Flow Matching approach to generate data living on SO(3) manifold.
- The second one is **FoldFlow-OT** which generalizes FoldFlow-base by drawing samples from a minibatch optimal transport coupling similarly to [OT-CFM](https://arxiv.org/abs/2302.00482).
- The second one is **FoldFlow-OT** which generalizes FoldFlow-base by drawing samples from a minibatch optimal transport coupling similar to [OT-CFM](https://arxiv.org/abs/2302.00482).
- The third one is **FoldFlow-SFM**, a stochastic version of FoldFlow-OT.

Our experiments include:
Expand Down Expand Up @@ -82,10 +82,10 @@ We welcome issues and pull requests (especially bug fixes) and contributions.
We will try our best to improve readability and answer questions!


## Licences
## Licenses
<p xmlns:cc="http://creativecommons.org/ns#" xmlns:dct="http://purl.org/dc/terms/"><a property="dct:title" rel="cc:attributionURL" href="https://github.com/Dreamfold/foldflow">FoldFlow</a> by <a rel="cc:attributionURL dct:creator" property="cc:attributionName" href="https://dreamfold.ai">Dreamfold</a> is licensed under <a href="http://creativecommons.org/licenses/by-nc/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer" style="display:inline-block;">Attribution-NonCommercial 4.0 International<img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1"><img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1"><img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/nc.svg?ref=chooser-v1"></a></p>

### Warning: the current code uses PyTorch 1.13 and torchdyn 1.0.6.

This code base is heavily inspired from the TorchCFM library! You can check Flow Matching with data living on Euclidean spaces there https://github.com/atong01/conditional-flow-matching
This code base is heavily inspired by the TorchCFM library! You can check Flow Matching with data living on Euclidean spaces there https://github.com/atong01/conditional-flow-matching