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    <description>Explore the world!</description>
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        <title>Graph Convolutional Networks</title>
        <description>
          Graph and Geometric Deep Learning - 
          1.Graph Convolutional Networks Zoo Graph convolutional networks papers Semi-Supervised Classification with Graph Convolutional Networks Learning Convolutional Neural Networks for Graphs Geometric deep learning: going beyond Euclidean data Deep Convolutional Networks on Graph-Structured Data Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering Spectral Networks and Deep Locally Connected Networks...
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        <pubDate>Tue, 10 Oct 2017 00:00:00 -0700</pubDate>
        <link>http://xmachinelearning.github.io/2017-10-10-GCN/</link>
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        <title>Missing View Imputation with Generative Adversarial Networks</title>
        <description>
          Domain mappings and view correspondences - 
          This package implements an approach for missing view and missing data imputation via generative adversarial networks (GANs), which we name as VIGAN. It combines cross-domain relations given unpaired data with multi-view relations given paired data. This approach first treats each view as a separate domain and identifies domain-to-domain mappings through...
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        <pubDate>Sun, 01 Oct 2017 00:00:00 -0700</pubDate>
        <link>http://xmachinelearning.github.io/2017-10-01-VIGAN/</link>
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        <title>Generative Adversarial Networks</title>
        <description>
          Papers and Codes. - 
          Generative Adversarial Networks (GANs) is a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather...
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        <pubDate>Thu, 07 Sep 2017 00:00:00 -0700</pubDate>
        <link>http://xmachinelearning.github.io/2017-09-07-GAN/</link>
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      <item>
        <title>Basic Materials for Deep Learning</title>
        <description>
          Books, Cources, Tutorials and Surveys. - 
          Here are some materials for deep learning. Free Books (1) Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville Link: http://www.deeplearningbook.org/ (2) Neural Network and Deep Learning (Book, Jan 2017), Michael Nielsen. Link: http://neuralnetworksanddeeplearning.com/index.html Courses Machine Learning - Stanford by Andrew Ng in Coursera Link: https://www.coursera.org/learn/machine-learning Tutorials (1) UFLDL...
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        <pubDate>Fri, 01 Sep 2017 00:00:00 -0700</pubDate>
        <link>http://xmachinelearning.github.io/2017-09-01-Deep-Learning/</link>
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        <title>PyTorch</title>
        <description>
          PyTorch Learning - 
          PyTorch is a python package that provides two high-level features: Tensor computation (like numpy) with strong GPU acceleration Deep Neural Networks built on a tape-based autograd system 1. PyTorch Installation How to install PyTorch? 2. Pytorch Tutorial Pytorch Tutorial The Incredible PyTorch: a curated list of tutorials, papers, projects, communities...
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        <pubDate>Thu, 13 Jul 2017 00:00:00 -0700</pubDate>
        <link>http://xmachinelearning.github.io/2017-07-13-pytorch-markdown/</link>
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        <title>TensorFlow</title>
        <description>
          Learn TensorFlow with examples - 
          TensorFlow is an open-source software library for machine learning across a range of tasks.

Installation

        </description>
        <pubDate>Sat, 01 Jul 2017 00:00:00 -0700</pubDate>
        <link>http://xmachinelearning.github.io/2017-07-01-tensorflow/</link>
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