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research/marco/README.md
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Automating the Evaluation of Crystallization Experiments Automating the Evaluation of Crystallization Experiments
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research/maskgan/README.md
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# MaskGAN: Better Text Generation via Filling in the ______ # MaskGAN: Better Text Generation via Filling in the ______
Code for [*MaskGAN: Better Text Generation via Filling in the Code for [*MaskGAN: Better Text Generation via Filling in the
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research/namignizer/README.md
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# Namignizer # 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). 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).
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research/neural_gpu/README.md
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# NeuralGPU # NeuralGPU
Code for the Neural GPU model described in http://arxiv.org/abs/1511.08228. 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. The extended version was described in https://arxiv.org/abs/1610.08613.
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research/neural_programmer/README.md
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# Neural Programmer # Neural Programmer
Implementation of the Neural Programmer model described in [paper](https://openreview.net/pdf?id=ry2YOrcge) Implementation of the Neural Programmer model described in [paper](https://openreview.net/pdf?id=ry2YOrcge)
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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> <font size=4><b>Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks.</b></font>
<b>Introduction</b> <b>Introduction</b>
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research/object_detection/README.md
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# Tensorflow Object Detection API # Tensorflow Object Detection API
Creating accurate machine learning models capable of localizing and identifying Creating accurate machine learning models capable of localizing and identifying
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research/pcl_rl/README.md
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Code for several RL algorithms used in the following papers: Code for several RL algorithms used in the following papers:
* "Improving Policy Gradient by Exploring Under-appreciated Rewards" by * "Improving Policy Gradient by Exploring Under-appreciated Rewards" by
Ofir Nachum, Mohammad Norouzi, and Dale Schuurmans. Ofir Nachum, Mohammad Norouzi, and Dale Schuurmans.
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research/qa_kg/README.md
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# Module networks for question answering on knowledge graph # Module networks for question answering on knowledge graph
This code repository contains a TensorFlow model for question answering on This code repository contains a TensorFlow model for question answering on
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research/real_nvp/README.md
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# Real NVP in TensorFlow # Real NVP in TensorFlow
*A Tensorflow implementation of the training procedure of* *A Tensorflow implementation of the training procedure of*
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research/sentiment_analysis/README.md
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# Sentiment Analysis # Sentiment Analysis
## Overview ## 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). 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).
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research/seq2species/README.md
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# Seq2Species: Neural Network Models for Species Classification # Seq2Species: Neural Network Models for Species Classification
*A deep learning solution for read-level taxonomic classification with 16s.* *A deep learning solution for read-level taxonomic classification with 16s.*
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research/skip_thoughts/README.md
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# Skip-Thought Vectors # Skip-Thought Vectors
This is a TensorFlow implementation of the model described in: This is a TensorFlow implementation of the model described in:
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research/steve/README.md
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# Stochastic Ensemble Value Expansion # Stochastic Ensemble Value Expansion
*A hybrid model-based/model-free reinforcement learning algorithm for sample-efficient continuous control.* *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. config file and a GPU, it takes the filename of the model to load as a mandatory third argument.
## Contact ## Contact
Please contact GitHub user buckman-google (jacobbuckman@gmail.com) with any questions. Please contact GitHub user buckman-google (jacobbuckman@gmail.com) with any questions.
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research/struct2depth/README.md
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# struct2depth # 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: 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:
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research/swivel/README.md
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# Swivel in Tensorflow # Swivel in Tensorflow
This is a [TensorFlow](http://www.tensorflow.org/) implementation of the This is a [TensorFlow](http://www.tensorflow.org/) implementation of the
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research/textsum/README.md
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Sequence-to-Sequence with Attention Model for Text Summarization. Sequence-to-Sequence with Attention Model for Text Summarization.
Authors: Authors:
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research/transformer/README.md
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# Spatial Transformer Network # Spatial Transformer Network
The Spatial Transformer Network [1] allows the spatial manipulation of data within the network. The Spatial Transformer Network [1] allows the spatial manipulation of data within the network.
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research/vid2depth/README.md
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# vid2depth # vid2depth
**Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints** **Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints**
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research/video_prediction/README.md
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# Video Prediction with Neural Advection # 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).* *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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