<commit_before>---
layout: archive
title: "Portfolio"
permalink: /portfolio/
author_profile: true
---

{% include base_path %}

<head>
  <meta http-equiv="content-type" content="text/html; charset=UTF-8">
  <meta name="viewport" content="width=device-width,initial-scale=1,maximum-scale=1">
  <link href="http://maxcdn.bootstrapcdn.com/font-awesome/4.3.0/css/font-awesome.min.css" rel="stylesheet">
  <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css">
  <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.1.1/jquery.min.js"></script>
  <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
  <link rel="icon" href="favicon.ico" type="image/x-icon">

<style>
.cliente {
    margin-top:10px;
    margin-right:10px;
    margin-left:10px;
    margin-bottom:10px;
    border: #cdcdcd medium solid;
    border-radius: 10px;
    -moz-border-radius: 10px;
    -webkit-border-radius: 10px;
}
</style>
</head>

<body>
  <script type="text/javascript" src="http://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
  <script type="text/javascript">
    $(document).ready(function() {
      var heights = $(".column").map(function() {
          return $(this).height();
        }).get(),

        maxHeight = Math.max.apply(null, heights);

      $(".column").height(maxHeight);
    });
  </script>
  <div class="container" style="width: 100%; height: 100vh;">
    <div class="row text-center">
      <h5>Label Key</h5>
      <span class="label label-info">Language</span>
      <span class="label label-success">Library</span>
      <span class="label label-danger">API</span>
      <span class="label label-warning">Project type</span>
      <span class="label label-primary">Subject Area</span>
    </div>
    <hr>
    <div class="row">
      <div class="col-md-4 column project">
        <div class="cliente">
        <h4 class="hackname">RBDoom</h4>
          <h5 class="link"><a href="https://github.com/sshkhr/RBDoom"><i class="fa fa-github"></i>GitHub</a>
        </h5>
        <hr>
        <p>Implemented a reinforcement learning based agent to play the first person shooter game DOOM using only image-pixel inputs. Used the RAINBOW algorithm based on deep q-learning and the VizDoom emulator for the game.</p>
        <span class="label label-info"><i class="fa fa-code"></i> Python</span>
        <span class="label label-success"><i class="fa fa-code"></i> Pytorch</span>
        <span class="label label-success"><i class="fa fa-code"></i> VizDoom</span>
        <span class="label label-primary"><i class="fa fa-code"></i> Reinforcement Learning</span>
      </div>
      </div>
      <div class="col-md-4 column project">
        <div class="cliente">
        <h4 class="hackname">Cross-Modal GAN</h4>
        <h5 class="link"><a href="https://github.com/sshkhr/cmGAN"><i class="fa fa-github"></i>GitHub</a>
        </h5>
        <hr>
        <p>Implemented the cross-modal GAN paper from "Cross-Modality Person Re-Id with Generative Adversarial Training" at IJCAI 2018. </p>
        <span class="label label-info"><i class="fa fa-code"></i> Python</span>
        <span class="label label-success"><i class="fa fa-code"></i> Pytorch</span>
        <span class="label label-primary"><i class="fa fa-code"></i> GAN</span>
      </div>
      </div>
      <div class="col-md-4 column project-right">
        <div class="cliente">
        <h4 class="hackname">Meta-Learning with Implicit Gradients</h4>
        <h5 class="link"><a href="https://github.com/sshkhr/imaml"><i class="fa fa-github"></i>GitHub</a>
        </h5>
        <h5 class="link"><a href="https://www.overleaf.com/read/pqmyyrnxsjmr"><i class="fa fa-share"></i>Report</a>
        </h5>
        <hr>
        <p>Implemented the NeurIPS 2019 paper using the Torchmeta and Higher librarries for meta-learning and higher-order gradients and replicated the results obtained by authors for few-shot learning on Ominglot dataset</p>
        <span class="label label-info"><i class="fa fa-code"></i> Python</span>
        <span class="label label-success"><i class="fa fa-code"></i> Pytorch</span>
        <span class="label label-warning"><i class="fa fa-cog"></i> Research </span>
        <span class="label label-primary"><i class="fa fa-code"></i> Meta-Learning</span>
      </div>
      </div>
    </div>
    </div>
</body><commit_msg>removed borders for now<commit_after>---
layout: archive
title: "Portfolio"
permalink: /portfolio/
author_profile: true
---

{% include base_path %}

<head>
  <meta http-equiv="content-type" content="text/html; charset=UTF-8">
  <meta name="viewport" content="width=device-width,initial-scale=1,maximum-scale=1">
  <link href="http://maxcdn.bootstrapcdn.com/font-awesome/4.3.0/css/font-awesome.min.css" rel="stylesheet">
  <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css">
  <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.1.1/jquery.min.js"></script>
  <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
  <link rel="icon" href="favicon.ico" type="image/x-icon">

<style>
.cliente {
    margin-top:10px;
    margin-right:10px;
    margin-left:10px;
    margin-bottom:10px;
    border: #cdcdcd medium solid;
    border-radius: 10px;
    -moz-border-radius: 10px;
    -webkit-border-radius: 10px;
}
</style>
</head>

<body>
  <script type="text/javascript" src="http://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
  <script type="text/javascript">
    $(document).ready(function() {
      var heights = $(".column").map(function() {
          return $(this).height();
        }).get(),

        maxHeight = Math.max.apply(null, heights);

      $(".column").height(maxHeight);
    });
  </script>
  <div class="container" style="width: 100%; height: 100vh;">
    <div class="row text-center">
      <h5>Label Key</h5>
      <span class="label label-info">Language</span>
      <span class="label label-success">Library</span>
      <span class="label label-danger">API</span>
      <span class="label label-warning">Project type</span>
      <span class="label label-primary">Subject Area</span>
    </div>
    <hr>
    <div class="row">
      <div class="col-md-4 column project">
        <h4 class="hackname">RBDoom</h4>
          <h5 class="link"><a href="https://github.com/sshkhr/RBDoom"><i class="fa fa-github"></i>GitHub</a>
        </h5>
        <hr>
        <p>Implemented a reinforcement learning based agent to play the first person shooter game DOOM using only image-pixel inputs. Used the RAINBOW algorithm based on deep q-learning and the VizDoom emulator for the game.</p>
        <span class="label label-info"><i class="fa fa-code"></i> Python</span>
        <span class="label label-success"><i class="fa fa-code"></i> Pytorch</span>
        <span class="label label-success"><i class="fa fa-code"></i> VizDoom</span>
        <span class="label label-primary"><i class="fa fa-code"></i> Reinforcement Learning</span>
      </div>
      <div class="col-md-4 column project">
        <h4 class="hackname">Cross-Modal GAN</h4>
        <h5 class="link"><a href="https://github.com/sshkhr/cmGAN"><i class="fa fa-github"></i>GitHub</a>
        </h5>
        <hr>
        <p>Implemented the cross-modal GAN paper from "Cross-Modality Person Re-Id with Generative Adversarial Training" at IJCAI 2018. </p>
        <span class="label label-info"><i class="fa fa-code"></i> Python</span>
        <span class="label label-success"><i class="fa fa-code"></i> Pytorch</span>
        <span class="label label-primary"><i class="fa fa-code"></i> GAN</span>
      </div>
      <div class="col-md-4 column project-right">
        <h4 class="hackname">Meta-Learning with Implicit Gradients</h4>
        <h5 class="link"><a href="https://github.com/sshkhr/imaml"><i class="fa fa-github"></i>GitHub</a>
        </h5>
        <h5 class="link"><a href="https://www.overleaf.com/read/pqmyyrnxsjmr"><i class="fa fa-share"></i>Report</a>
        </h5>
        <hr>
        <p>Implemented the NeurIPS 2019 paper using the Torchmeta and Higher librarries for meta-learning and higher-order gradients and replicated the results obtained by authors for few-shot learning on Ominglot dataset</p>
        <span class="label label-info"><i class="fa fa-code"></i> Python</span>
        <span class="label label-success"><i class="fa fa-code"></i> Pytorch</span>
        <span class="label label-warning"><i class="fa fa-cog"></i> Research </span>
        <span class="label label-primary"><i class="fa fa-code"></i> Meta-Learning</span>
      </div>
    </div>
    </div>
</body>
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