Update README.md

README.md

@@ -8,16 +8,23 @@ To propose a model for inclusion please submit a pull request.
 
 
 ## Models
-- [autoencoder](autoencoder) -- various autoencoders
-- [differential_privacy](differential_privacy) -- privacy-preserving student models from multiple teachers
+- [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
-- [namignizer](namignizer) -- recognize and generate names
-- [neural_gpu](neural_gpu) -- highly parallel neural computer
+- [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.
-- [resnet](resnet) -- deep and wide residual networks
-- [slim](slim) -- image classification models in TF-Slim
-- [swivel](swivel) -- the Swivel algorithm for generating word embeddings
-- [syntaxnet](syntaxnet) -- neural models of natural language syntax
+- [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
+- [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.