Updated README files of research models (#8390)

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research/adv_imagenet_models/README.md

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 # Adversarially trained ImageNet models
 
 Pre-trained ImageNet models from the following papers:

research/adversarial_crypto/README.md

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 # Learning to Protect Communications with Adversarial Neural Cryptography
 
 This is a slightly-updated model used for the paper

research/adversarial_logit_pairing/README.md

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 # Adversarial logit pairing
 

research/adversarial_text/README.md

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 # Adversarial Text Classification
 
 Code for [*Adversarial Training Methods for Semi-Supervised Text Classification*](https://arxiv.org/abs/1605.07725) and [*Semi-Supervised Sequence Learning*](https://arxiv.org/abs/1511.01432).

research/audioset/README.md

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 # Models for AudioSet: A Large Scale Dataset of Audio Events
 
 This repository provides models and supporting code associated with

research/autoencoder/README.md

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research/cognitive_mapping_and_planning/README.md

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 # Cognitive Mapping and Planning for Visual Navigation
 **Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, Jitendra Malik**
 

research/deep_speech/README.md

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 # DeepSpeech2 Model
+
 ## Overview
 This is an implementation of the [DeepSpeech2](https://arxiv.org/pdf/1512.02595.pdf) model. Current implementation is based on the code from the authors' [DeepSpeech code](https://github.com/PaddlePaddle/DeepSpeech) and the implementation in the [MLPerf Repo](https://github.com/mlperf/reference/tree/master/speech_recognition).
 

research/delf/README.md

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 # DELF: DEep Local Features
 
 This project presents code for extracting DELF features, which were introduced

research/domain_adaptation/README.md

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 ## Introduction
 This is the code used for two domain adaptation papers.
 

research/efficient-hrl/README.md

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 Code for performing Hierarchical RL based on the following publications:
 
 "Data-Efficient Hierarchical Reinforcement Learning" by

research/fivo/README.md

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 # Filtering Variational Objectives
 
 This folder contains a TensorFlow implementation of the algorithms from
@@ -208,4 +212,4 @@ This codebase comes with a number of tests to verify correctness, runnable via `
 
 ### Contact
 
-This codebase is maintained by Dieterich Lawson, reachable via email at dieterichl@google.com. For questions and issues please open an issue on the tensorflow/models issues tracker and assign it to @dieterichlawson.
+This codebase is maintained by Dieterich Lawson. For questions and issues please open an issue on the tensorflow/models issues tracker and assign it to @dieterichlawson.

research/global_objectives/README.md

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 # Global Objectives
 The Global Objectives library provides TensorFlow loss functions that optimize
 directly for a variety of objectives including AUC, recall at precision, and

research/im2txt/README.md

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 # Show and Tell: A Neural Image Caption Generator
 
 A TensorFlow implementation of the image-to-text model described in the paper:

research/inception/README.md

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+
 **NOTE: For the most part, you will find a newer version of this code at [models/research/slim](https://github.com/tensorflow/models/tree/master/research/slim).** In particular:
 
 *   `inception_train.py` and `imagenet_train.py` should no longer be used. The slim editions for running on multiple GPUs are the current best examples.

research/learned_optimizer/README.md

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 # Learned Optimizer
 
 Code for [Learned Optimizers that Scale and Generalize](https://arxiv.org/abs/1703.04813).

research/learning_to_remember_rare_events/README.md

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+
+---
+
 Code for the Memory Module as described
 in "Learning to Remember Rare Events" by
 Lukasz Kaiser, Ofir Nachum, Aurko Roy, and Samy Bengio

research/lexnet_nc/README.md

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 # LexNET for Noun Compound Relation Classification
 
 This is a [Tensorflow](http://www.tensorflow.org/) implementation of the LexNET

research/lm_1b/README.md

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 <font size=4><b>Language Model on One Billion Word Benchmark</b></font>
 
 <b>Authors:</b>

research/lm_commonsense/README.md

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 # A Simple Method for Commonsense Reasoning
 
 This repository contains code to reproduce results from [*A Simple Method for Commonsense Reasoning*](https://arxiv.org/abs/1806.02847).