Updated README files of research models (#8390)

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research/marco/README.md

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 Automating the Evaluation of Crystallization Experiments
 ========================================================
 

research/maskgan/README.md

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 # MaskGAN: Better Text Generation via Filling in the ______
 
 Code for [*MaskGAN: Better Text Generation via Filling in the

research/namignizer/README.md

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 # Namignizer
 
 Use a variation of the [PTB](https://www.tensorflow.org/versions/r0.8/tutorials/recurrent/index.html#recurrent-neural-networks) model to recognize and generate names using the [Kaggle Baby Name Database](https://www.kaggle.com/kaggle/us-baby-names).

research/neural_gpu/README.md

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 # NeuralGPU
 Code for the Neural GPU model described in http://arxiv.org/abs/1511.08228.
 The extended version was described in https://arxiv.org/abs/1610.08613.

research/neural_programmer/README.md

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 # Neural Programmer
 
 Implementation of the Neural Programmer model described in [paper](https://openreview.net/pdf?id=ry2YOrcge)

research/next_frame_prediction/README.md

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 <font size=4><b>Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks.</b></font>
 
 <b>Introduction</b>

research/object_detection/README.md

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 # Tensorflow Object Detection API
 Creating accurate machine learning models capable of localizing and identifying

research/pcl_rl/README.md

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 Code for several RL algorithms used in the following papers:
 * "Improving Policy Gradient by Exploring Under-appreciated Rewards" by
 Ofir Nachum, Mohammad Norouzi, and Dale Schuurmans.

research/qa_kg/README.md

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 # Module networks for question answering on knowledge graph
 
 This code repository contains a TensorFlow model for question answering on

research/real_nvp/README.md

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 # Real NVP in TensorFlow
 
 *A Tensorflow implementation of the training procedure of*

research/sentiment_analysis/README.md

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 # Sentiment Analysis
 ## Overview
 This is an implementation of the Sentiment Analysis model as described in the [this paper](https://arxiv.org/abs/1412.1058). The implementation is with the reference to [paddle version](https://github.com/mlperf/reference/tree/master/sentiment_analysis/paddle).

research/seq2species/README.md

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 # Seq2Species: Neural Network Models for Species Classification
 
 *A deep learning solution for read-level taxonomic classification with 16s.*

research/skip_thoughts/README.md

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 # Skip-Thought Vectors
 
 This is a TensorFlow implementation of the model described in:

research/steve/README.md

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 # Stochastic Ensemble Value Expansion
 
 *A hybrid model-based/model-free reinforcement learning algorithm for sample-efficient continuous control.*
@@ -87,4 +91,4 @@ toy examples in the first segment of the paper.
 config file and a GPU, it takes the filename of the model to load as a mandatory third argument.
 
 ## Contact
-Please contact GitHub user buckman-google (jacobbuckman@gmail.com) with any questions.
\ No newline at end of file
+Please contact GitHub user buckman-google (jacobbuckman@gmail.com) with any questions.

research/struct2depth/README.md

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 # struct2depth
 
 This a method for unsupervised learning of depth and egomotion from monocular video, achieving new state-of-the-art results on both tasks by explicitly modeling 3D object motion, performing on-line refinement and improving quality for moving objects by novel loss formulations. It will appear in the following paper: 

research/swivel/README.md

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 # Swivel in Tensorflow
 
 This is a [TensorFlow](http://www.tensorflow.org/) implementation of the

research/textsum/README.md

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 Sequence-to-Sequence with Attention Model for Text Summarization.
 
 Authors:

research/transformer/README.md

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 # Spatial Transformer Network
 
 The Spatial Transformer Network [1] allows the spatial manipulation of data within the network.

research/vid2depth/README.md

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 # vid2depth
 
 **Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints**

research/video_prediction/README.md

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 # Video Prediction with Neural Advection
 
 *A TensorFlow implementation of the models described in [Unsupervised Learning for Physical Interaction through Video Prediction (Finn et al., 2016)](https://arxiv.org/abs/1605.07157).*