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research/adv_imagenet_models/README.md
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# Adversarially trained ImageNet models # Adversarially trained ImageNet models
Pre-trained ImageNet models from the following papers: Pre-trained ImageNet models from the following papers:
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research/adversarial_crypto/README.md
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# Learning to Protect Communications with Adversarial Neural Cryptography # Learning to Protect Communications with Adversarial Neural Cryptography
This is a slightly-updated model used for the paper This is a slightly-updated model used for the paper
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research/adversarial_logit_pairing/README.md
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# Adversarial logit pairing # Adversarial logit pairing
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research/adversarial_text/README.md
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# Adversarial Text Classification # 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). 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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research/audioset/README.md
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# Models for AudioSet: A Large Scale Dataset of Audio Events # Models for AudioSet: A Large Scale Dataset of Audio Events
This repository provides models and supporting code associated with This repository provides models and supporting code associated with
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research/autoencoder/README.md 0 → 100644
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research/cognitive_mapping_and_planning/README.md
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# Cognitive Mapping and Planning for Visual Navigation # Cognitive Mapping and Planning for Visual Navigation
**Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, Jitendra Malik** **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 # DeepSpeech2 Model
## Overview ## 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). 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 # DELF: DEep Local Features
This project presents code for extracting DELF features, which were introduced This project presents code for extracting DELF features, which were introduced
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research/domain_adaptation/README.md
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## Introduction ## Introduction
This is the code used for two domain adaptation papers. 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: Code for performing Hierarchical RL based on the following publications:
"Data-Efficient Hierarchical Reinforcement Learning" by "Data-Efficient Hierarchical Reinforcement Learning" by
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research/fivo/README.md
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# Filtering Variational Objectives # Filtering Variational Objectives
This folder contains a TensorFlow implementation of the algorithms from This folder contains a TensorFlow implementation of the algorithms from
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### Contact ### 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 # Global Objectives
The Global Objectives library provides TensorFlow loss functions that optimize The Global Objectives library provides TensorFlow loss functions that optimize
directly for a variety of objectives including AUC, recall at precision, and directly for a variety of objectives including AUC, recall at precision, and
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research/im2txt/README.md
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# Show and Tell: A Neural Image Caption Generator # Show and Tell: A Neural Image Caption Generator
A TensorFlow implementation of the image-to-text model described in the paper: A TensorFlow implementation of the image-to-text model described in the paper:
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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: **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. * `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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research/learned_optimizer/README.md
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# Learned Optimizer # Learned Optimizer
Code for [Learned Optimizers that Scale and Generalize](https://arxiv.org/abs/1703.04813). Code for [Learned Optimizers that Scale and Generalize](https://arxiv.org/abs/1703.04813).
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research/learning_to_remember_rare_events/README.md
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Code for the Memory Module as described Code for the Memory Module as described
in "Learning to Remember Rare Events" by in "Learning to Remember Rare Events" by
Lukasz Kaiser, Ofir Nachum, Aurko Roy, and Samy Bengio Lukasz Kaiser, Ofir Nachum, Aurko Roy, and Samy Bengio
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research/lexnet_nc/README.md
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# LexNET for Noun Compound Relation Classification # LexNET for Noun Compound Relation Classification
This is a [Tensorflow](http://www.tensorflow.org/) implementation of the LexNET This is a [Tensorflow](http://www.tensorflow.org/) implementation of the LexNET
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research/lm_1b/README.md
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<font size=4><b>Language Model on One Billion Word Benchmark</b></font> <font size=4><b>Language Model on One Billion Word Benchmark</b></font>
<b>Authors:</b> <b>Authors:</b>
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research/lm_commonsense/README.md
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# A Simple Method for Commonsense Reasoning # 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). This repository contains code to reproduce results from [*A Simple Method for Commonsense Reasoning*](https://arxiv.org/abs/1806.02847).
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