Include link to posters

README.md

@@ -5,6 +5,7 @@
 This is the official implementation of our NeurIPS 2019 paper [Deep Set Prediction Networks][0].
 We propose a new way of predicting sets with a neural network that doesn't suffer from discontinuity issues.
 This is done by backpropagating through a set encoder to act as a set decoder.
+You can take a look at the [poster for NeurIPS 2019][4] or the [poster for the NeurIPS 2019 workshop on Sets & Partitions][5].
 
 To use the decoder, you only need [`dspn.py`][1].
 You can see how it is used in [`model.py`][2] with `build_net` and the `Net` class.
@@ -29,3 +30,5 @@ You can download pre-trained models and the predictions thereof from the [Resour
 [1]: https://github.com/Cyanogenoid/dspn/blob/master/dspn.py
 [2]: https://github.com/Cyanogenoid/dspn/blob/master/dspn/model.py
 [3]: https://github.com/Cyanogenoid/dspn/releases/tag/resources
+[4]: https://www.cyanogenoid.com/files/dspn-poster.pdf
+[5]: https://www.cyanogenoid.com/files/dspn-workshop-poster.pdf