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Updated README files of research models (#8390)

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# Adversarially trained ImageNet models
Pre-trained ImageNet models from the following papers:
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# Learning to Protect Communications with Adversarial Neural Cryptography
This is a slightly-updated model used for the paper
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# Adversarial logit pairing
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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).
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# Models for AudioSet: A Large Scale Dataset of Audio Events
This repository provides models and supporting code associated with
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# Cognitive Mapping and Planning for Visual Navigation
**Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, Jitendra Malik**
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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).
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# DELF: DEep Local Features
This project presents code for extracting DELF features, which were introduced
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## Introduction
This is the code used for two domain adaptation papers.
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Code for performing Hierarchical RL based on the following publications:
"Data-Efficient Hierarchical Reinforcement Learning" by
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# Filtering Variational Objectives
This folder contains a TensorFlow implementation of the algorithms from
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### 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.
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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
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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:
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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.
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# Learned Optimizer
Code for [Learned Optimizers that Scale and Generalize](https://arxiv.org/abs/1703.04813).
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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
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# LexNET for Noun Compound Relation Classification
This is a [Tensorflow](http://www.tensorflow.org/) implementation of the LexNET
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<font size=4><b>Language Model on One Billion Word Benchmark</b></font>
<b>Authors:</b>
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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).
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