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Move the research models into a research subfolder (tensorflow#2430)
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nealwu authored Sep 21, 2017
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2 changes: 1 addition & 1 deletion .gitmodules
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[submodule "tensorflow"]
path = syntaxnet/tensorflow
path = research/syntaxnet/tensorflow
url = https://github.com/tensorflow/tensorflow.git
70 changes: 35 additions & 35 deletions CODEOWNERS
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adversarial_crypto/* @dave-andersen
adversarial_text/* @rsepassi
adv_imagenet_models/* @AlexeyKurakin
attention_ocr/* @alexgorban
audioset/* @plakal @dpwe
autoencoders/* @snurkabill
cognitive_mapping_and_planning/* @s-gupta
compression/* @nmjohn
differential_privacy/* @panyx0718
domain_adaptation/* @bousmalis @ddohan
im2txt/* @cshallue
inception/* @shlens @vincentvanhoucke
learned_optimizer/* @olganw @nirum
learning_to_remember_rare_events/* @lukaszkaiser @ofirnachum
lfads/* @jazcollins @susillo
lm_1b/* @oriolvinyals @panyx0718
namignizer/* @knathanieltucker
neural_gpu/* @lukaszkaiser
neural_programmer/* @arvind2505
next_frame_prediction/* @panyx0718
object_detection/* @jch1 @tombstone @derekjchow @jesu9 @dreamdragon
pcl_rl/* @ofirnachum
ptn/* @xcyan @arkanath @hellojas @honglaklee
real_nvp/* @laurent-dinh
rebar/* @gjtucker
resnet/* @panyx0718
skip_thoughts/* @cshallue
slim/* @sguada @nathansilberman
street/* @theraysmith
swivel/* @waterson
syntaxnet/* @calberti @andorardo
textsum/* @panyx0718 @peterjliu
transformer/* @daviddao
official/* @nealwu @jhseu @itsmeolivia
research/adversarial_crypto/* @dave-andersen
research/adversarial_text/* @rsepassi
research/adv_imagenet_models/* @AlexeyKurakin
research/attention_ocr/* @alexgorban
research/audioset/* @plakal @dpwe
research/autoencoders/* @snurkabill
research/cognitive_mapping_and_planning/* @s-gupta
research/compression/* @nmjohn
research/differential_privacy/* @panyx0718
research/domain_adaptation/* @bousmalis @ddohan
research/im2txt/* @cshallue
research/inception/* @shlens @vincentvanhoucke
research/learned_optimizer/* @olganw @nirum
research/learning_to_remember_rare_events/* @lukaszkaiser @ofirnachum
research/lfads/* @jazcollins @susillo
research/lm_1b/* @oriolvinyals @panyx0718
research/namignizer/* @knathanieltucker
research/neural_gpu/* @lukaszkaiser
research/neural_programmer/* @arvind2505
research/next_frame_prediction/* @panyx0718
research/object_detection/* @jch1 @tombstone @derekjchow @jesu9 @dreamdragon
research/pcl_rl/* @ofirnachum
research/ptn/* @xcyan @arkanath @hellojas @honglaklee
research/real_nvp/* @laurent-dinh
research/rebar/* @gjtucker
research/resnet/* @panyx0718
research/skip_thoughts/* @cshallue
research/slim/* @sguada @nathansilberman
research/street/* @theraysmith
research/swivel/* @waterson
research/syntaxnet/* @calberti @andorardo
research/textsum/* @panyx0718 @peterjliu
research/transformer/* @daviddao
research/video_prediction/* @cbfinn
tutorials/embedding/* @zffchen78 @a-dai
tutorials/image/* @sherrym @shlens
tutorials/rnn/* @lukaszkaiser @ebrevdo
video_prediction/* @cbfinn

46 changes: 4 additions & 42 deletions README.md
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# TensorFlow Models

This repository contains machine learning models implemented in
[TensorFlow](https://tensorflow.org). The models are maintained by their
respective authors. To propose a model for inclusion, please submit a pull
request.
This repository contains a number of different models implemented in [TensorFlow](https://tensorflow.org):

Currently, the models are compatible with TensorFlow 1.0 or later. If you are
running TensorFlow 0.12 or earlier, please
[upgrade your installation](https://www.tensorflow.org/install).
The [official models](official) are a collection of example models that use TensorFlow's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest stable TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read. We especially recommend newer TensorFlow users to start here.

The [research models](research) are a large collection of models implemented in TensorFlow by researchers.

## Models
- [adversarial_crypto](adversarial_crypto): protecting communications with adversarial neural cryptography.
- [adversarial_text](adversarial_text): semi-supervised sequence learning with adversarial training.
- [attention_ocr](attention_ocr): a model for real-world image text extraction.
- [audioset](audioset): Models and supporting code for use with [AudioSet](http://g.co.audioset).
- [autoencoder](autoencoder): various autoencoders.
- [cognitive_mapping_and_planning](cognitive_mapping_and_planning): implementation of a spatial memory based mapping and planning architecture for visual navigation.
- [compression](compression): compressing and decompressing images using a pre-trained Residual GRU network.
- [differential_privacy](differential_privacy): privacy-preserving student models from multiple teachers.
- [domain_adaptation](domain_adaptation): domain separation networks.
- [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.
- [lfads](lfads): sequential variational autoencoder for analyzing neuroscience data.
- [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.
- [object_detection](object_detection): localizing and identifying multiple objects in a single image.
- [pcl_rl](pcl_rl): code for several reinforcement learning algorithms, including Path Consistency Learning.
- [ptn](ptn): perspective transformer nets for 3D object reconstruction.
- [qa_kg](qa_kg): module networks for question answering on knowledge graphs.
- [real_nvp](real_nvp): density estimation using real-valued non-volume preserving (real NVP) transformations.
- [rebar](rebar): low-variance, unbiased gradient estimates for discrete latent variable models.
- [resnet](resnet): deep and wide residual networks.
- [skip_thoughts](skip_thoughts): recurrent neural network sentence-to-vector encoder.
- [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.
The [tutorial models](tutorials) are models described in the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/).
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# TensorFlow Research Models

This folder contains machine learning models implemented by researchers in
[TensorFlow](https://tensorflow.org). The models are maintained by their
respective authors. To propose a model for inclusion, please submit a pull
request.

Currently, the models are compatible with TensorFlow 1.0 or later. If you are
running TensorFlow 0.12 or earlier, please
[upgrade your installation](https://www.tensorflow.org/install).


## Models
- [adversarial_crypto](adversarial_crypto): protecting communications with adversarial neural cryptography.
- [adversarial_text](adversarial_text): semi-supervised sequence learning with adversarial training.
- [attention_ocr](attention_ocr): a model for real-world image text extraction.
- [audioset](audioset): Models and supporting code for use with [AudioSet](http://g.co.audioset).
- [autoencoder](autoencoder): various autoencoders.
- [cognitive_mapping_and_planning](cognitive_mapping_and_planning): implementation of a spatial memory based mapping and planning architecture for visual navigation.
- [compression](compression): compressing and decompressing images using a pre-trained Residual GRU network.
- [differential_privacy](differential_privacy): privacy-preserving student models from multiple teachers.
- [domain_adaptation](domain_adaptation): domain separation networks.
- [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.
- [lfads](lfads): sequential variational autoencoder for analyzing neuroscience data.
- [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.
- [object_detection](object_detection): localizing and identifying multiple objects in a single image.
- [pcl_rl](pcl_rl): code for several reinforcement learning algorithms, including Path Consistency Learning.
- [ptn](ptn): perspective transformer nets for 3D object reconstruction.
- [qa_kg](qa_kg): module networks for question answering on knowledge graphs.
- [real_nvp](real_nvp): density estimation using real-valued non-volume preserving (real NVP) transformations.
- [rebar](rebar): low-variance, unbiased gradient estimates for discrete latent variable models.
- [resnet](resnet): deep and wide residual networks.
- [skip_thoughts](skip_thoughts): recurrent neural network sentence-to-vector encoder.
- [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.
- [video_prediction](video_prediction): predicting future video frames with neural advection.
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