feedback link

labml_nn/__init__.py

@@ -22,6 +22,7 @@ and
 [relative multi-headed attention](https://lab-ml.com/labml_nn/transformers/relative_mha.html).
 
 * [kNN-LM: Generalization through Memorization](https://lab-ml.com/labml_nn/transformers/knn)
+* [Feedback Transformer](https://lab-ml.com/labml_nn/transformers/feedback)
 
 #### ✨ [Recurrent Highway Networks](https://lab-ml.com/labml_nn/recurrent_highway_networks)
 

labml_nn/hypernetworks/hyper_lstm.py

 """
 ---
-title: HyperNetworks
+title: HyperNetworks - HyperLSTM
 summary: A PyTorch implementation/tutorial of HyperLSTM introduced in paper HyperNetworks.
 ---
 
-# HyperNetworks
+# HyperNetworks - HyperLSTM
 
 We have implemented HyperLSTM introduced in paper
 [HyperNetworks](https://arxiv.org/abs/1609.09106), with annotations.

labml_nn/transformers/__init__.py

@@ -21,7 +21,12 @@ and derivatives and enhancements of it.
 ## [kNN-LM](knn)
 
 This is an implementation of the paper
- [Generalization through Memorization: Nearest Neighbor Language Models](https://arxiv.org/abs/1911.00172).
+[Generalization through Memorization: Nearest Neighbor Language Models](https://arxiv.org/abs/1911.00172).
+
+## [Feedback Transformer](feedback)
+
+This is an implementation of the paper
+[Accessing Higher-level Representations in Sequential Transformers with Feedback Memory](https://arxiv.org/abs/2002.09402).
 """
 
 from .configs import TransformerConfigs

readme.md

@@ -23,6 +23,7 @@ and
 [relative multi-headed attention](https://lab-ml.com/labml_nn/transformers/relative_mha.html).
 
 * [kNN-LM: Generalization through Memorization](https://lab-ml.com/labml_nn/transformers/knn)
+* [Feedback Transformer](https://lab-ml.com/labml_nn/transformers/feedback)
 
 #### ✨ [Recurrent Highway Networks](https://lab-ml.com/labml_nn/recurrent_highway_networks)