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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
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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
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This is a slightly-updated model used for the paper

‎research/adversarial_logit_pairing/README.md

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# Adversarial logit pairing
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‎research/adversarial_text/README.md

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# Adversarial Text Classification
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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
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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
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**Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, Jitendra Malik**
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‎research/deep_speech/README.md

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# DeepSpeech2 Model
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## Overview
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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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‎research/delf/README.md

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

‎research/domain_adaptation/README.md

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## Introduction
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This is the code used for two domain adaptation papers.
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‎research/efficient-hrl/README.md

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

‎research/fivo/README.md

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# Filtering Variational Objectives
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This folder contains a TensorFlow implementation of the algorithms from
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### Contact
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This codebase is maintained by Dieterich Lawson, reachable via email at [email protected]. For questions and issues please open an issue on the tensorflow/models issues tracker and assign it to @dieterichlawson.
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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
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The Global Objectives library provides TensorFlow loss functions that optimize
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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
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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:
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* `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
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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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---
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Code for the Memory Module as described
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in "Learning to Remember Rare Events" by
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Lukasz Kaiser, Ofir Nachum, Aurko Roy, and Samy Bengio

‎research/lexnet_nc/README.md

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# LexNET for Noun Compound Relation Classification
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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>
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<b>Authors:</b>

‎research/lm_commonsense/README.md

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

‎research/marco/README.md

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Automating the Evaluation of Crystallization Experiments
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========================================================
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‎research/maskgan/README.md

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

‎research/namignizer/README.md

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

‎research/neural_programmer/README.md

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# Neural Programmer
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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>
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<b>Introduction</b>

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

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Code for several RL algorithms used in the following papers:
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* "Improving Policy Gradient by Exploring Under-appreciated Rewards" by
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‎research/qa_kg/README.md

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

‎research/real_nvp/README.md

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

‎research/sentiment_analysis/README.md

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# Sentiment Analysis
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## Overview
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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
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*A deep learning solution for read-level taxonomic classification with 16s.*

‎research/skip_thoughts/README.md

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

‎research/steve/README.md

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# Stochastic Ensemble Value Expansion
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*A hybrid model-based/model-free reinforcement learning algorithm for sample-efficient continuous control.*
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config file and a GPU, it takes the filename of the model to load as a mandatory third argument.
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## Contact
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Please contact GitHub user buckman-google ([email protected]) with any questions.
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Please contact GitHub user buckman-google ([email protected]) with any questions.

‎research/struct2depth/README.md

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# struct2depth
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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
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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.
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Authors:

‎research/transformer/README.md

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

‎research/vid2depth/README.md

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# vid2depth
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**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
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*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).*

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