Cleaning up for the main README

README.md

@@ -8,23 +8,24 @@ To propose a model for inclusion please submit a pull request.
 
 
 ## Models
-- [autoencoder](autoencoder) -- various autoencoders.
-- [compression](compression) -- compressing and decompressing images using pre-trained Residual GRU network.
-- [differential_privacy](differential_privacy) -- privacy-preserving student models from multiple teachers.
-- [im2txt](im2txt) -- image-to-text neural network for image captioning.
-- [lm_1b](lm_1b) -- language modelling on one billion word benchmark.
-- [inception](inception) -- deep convolutional networks for computer vision.
-- [namignizer](namignizer) -- recognize and generate names.
-- [neural_gpu](neural_gpu) -- highly parallel neural computer.
-- [neural_programmer](neural_programmer) -- neural network augmented with logic and mathematic operations.
-- [next_frame_prediction](next_frame_prediction) --  probabilistic future frame synthesis via cross convolutional networks.
-- [real_nvp](real_nvp) -- density estimation using real-valued non-volume preserving (real NVP).
-- [resnet](resnet) -- deep and wide residual networks.
-- [slim](slim) -- image classification models in TF-Slim.
-- [street](street) -- identify the name of a street (in France) from an image using Deep RNN.
-- [swivel](swivel) -- the Swivel algorithm for generating word embeddings.
-- [syntaxnet](syntaxnet) -- neural models of natural language syntax.
-- [textsum](textsum) -- sequence-to-sequence with attention model for text summarization.
-- [transformer](transformer) -- spatial transformer network, which allows the spatial manipulation of data within the network.
-- [tutorials](tutorials) -- models referenced to from the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/).
-- [video_prediction](video_prediction) -- predicting future video frames with neural advection.
+- [autoencoder](autoencoder): various autoencoders.
+- [compression](compression): compressing and decompressing images using pre-trained Residual GRU network.
+- [differential_privacy](differential_privacy): privacy-preserving student models from multiple teachers.
+- [im2txt](im2txt): image-to-text neural network for image captioning.
+- [inception](inception): deep convolutional networks for computer vision.
+- [learning_to_remember_rare_events](learning_to_remember_rare_events):  a large-scale life-long memory module for use in deep learning.
+- [lm_1b](lm_1b): language modeling on the one billion word benchmark.
+- [namignizer](namignizer): recognize and generate names.
+- [neural_gpu](neural_gpu): highly parallel neural computer.
+- [neural_programmer](neural_programmer): neural network augmented with logic and mathematic operations.
+- [next_frame_prediction](next_frame_prediction): probabilistic future frame synthesis via cross convolutional networks.
+- [real_nvp](real_nvp): density estimation using real-valued non-volume preserving (real NVP) transformations.
+- [resnet](resnet): deep and wide residual networks.
+- [slim](slim): image classification models in TF-Slim.
+- [street](street): identify the name of a street (in France) from an image using a Deep RNN.
+- [swivel](swivel): the Swivel algorithm for generating word embeddings.
+- [syntaxnet](syntaxnet): neural models of natural language syntax.
+- [textsum](textsum): sequence-to-sequence with attention model for text summarization.
+- [transformer](transformer): spatial transformer network, which allows the spatial manipulation of data within the network.
+- [tutorials](tutorials): models described in the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/).
+- [video_prediction](video_prediction): predicting future video frames with neural advection.