Update documentation with link to paper

Author: William de Vazelhes

.buildinfo

@@ -59,13 +59,13 @@ Below is a gallery of example metric-learn use cases.
 
   .. container:: sphx-glr-download
 
-    :download:`Download all examples in Python source code: auto_examples_python.zip <//home/will/new_repo/metric-learn/doc/auto_examples/auto_examples_python.zip>`
+    :download:`Download all examples in Python source code: auto_examples_python.zip <//home/will/metric-learn/doc/auto_examples/auto_examples_python.zip>`
 
 
 
   .. container:: sphx-glr-download
 
-    :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip <//home/will/new_repo/metric-learn/doc/auto_examples/auto_examples_jupyter.zip>`
+    :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip <//home/will/metric-learn/doc/auto_examples/auto_examples_jupyter.zip>`
 
 
 .. only:: html

_downloads/7bad39157ddf73bd946aceb8bc69eac4/auto_examples_jupyter.zip renamed to _downloads/195613ed69dc7de9449134bf862928f9/auto_examples_jupyter.zip

@@ -683,9 +683,9 @@ Now that we've created our constraints, let's see what it looks like!
      [ 0  9]
      [ 0 10]
      ...
-     [19  2]
-     [42 40]
-     [82 41]]
+     [35 23]
+     [52 64]
+     [86 63]]
     [ 1.  1.  1. ... -1. -1. -1.]
 
 
@@ -744,7 +744,7 @@ metric-learn with scikit-learn estimators.
 This brings us to the end of this tutorial! Have fun Metric Learning :)
 
 
-**Total running time of the script:** ( 1 minutes  19.674 seconds)
+**Total running time of the script:** ( 1 minutes  15.365 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_metric_learning_examples.py:

_downloads/2bcf5e32b5d52d84565bc06c5ad91c98/plot_sandwich.ipynb renamed to _downloads/1fbc987b969eaee0a05177c2022570ae/plot_sandwich.ipynb

@@ -116,7 +116,7 @@ Sandwich demo based on code from http://nbviewer.ipython.org/6576096
     if __name__ == '__main__':
       sandwich_demo()
 
-**Total running time of the script:** ( 0 minutes  4.620 seconds)
+**Total running time of the script:** ( 0 minutes  4.099 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_sandwich.py:

_downloads/5632643bbb0183e351bf2b4dc2ab7c39/plot_sandwich.py renamed to _downloads/4228f005bd3fbc4a86c3ac1e76e0c591/plot_sandwich.py

@@ -7,10 +7,28 @@ contains efficient Python implementations of several popular supervised and
 weakly-supervised metric learning algorithms. As part of `scikit-learn-contrib
 <https://github.com/scikit-learn-contrib>`_, the API of metric-learn is compatible with `scikit-learn
 <https://scikit-learn.org/>`_, the leading library for machine learning in
-Python. This allows to use of all the scikit-learn routines (for pipelining,
+Python. This allows to use all the scikit-learn routines (for pipelining,
 model selection, etc) with metric learning algorithms through a unified
 interface.
 
+If you use metric-learn in a scientific publication, we would appreciate
+citations to the following paper:
+
+`metric-learn: Metric Learning Algorithms in Python
+<https://arxiv.org/abs/1908.04710>`_, de Vazelhes
+*et al.*, arXiv:1908.04710, 2019.
+
+Bibtex entry::
+
+  @techreport{metric-learn,
+    title = {metric-learn: {M}etric {L}earning {A}lgorithms in {P}ython},
+    author = {{de Vazelhes}, William and {Carey}, CJ and {Tang}, Yuan and
+              {Vauquier}, Nathalie and {Bellet}, Aur{\'e}lien},
+    institution = {arXiv:1908.04710},
+    year = {2019}
+  }
+
+
 Documentation outline
 ---------------------
 

_downloads/8effe4b984579ff0eb0e570d9d224324/auto_examples_python.zip renamed to _downloads/6ba8271a51eacf71a8f143ee6489f1f1/auto_examples_python.zip

@@ -178,9 +178,9 @@ <h1>Examples<a class="headerlink" href="#examples" title="Permalink to this head
 </div>
 <div style='clear:both'></div><div class="sphx-glr-footer class sphx-glr-footer-gallery docutils container">
 <div class="sphx-glr-download docutils container">
-<a class="reference download internal" download="" href="../_downloads/8effe4b984579ff0eb0e570d9d224324/auto_examples_python.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">all</span> <span class="pre">examples</span> <span class="pre">in</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">auto_examples_python.zip</span></code></a></div>
+<a class="reference download internal" download="" href="../_downloads/6ba8271a51eacf71a8f143ee6489f1f1/auto_examples_python.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">all</span> <span class="pre">examples</span> <span class="pre">in</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">auto_examples_python.zip</span></code></a></div>
 <div class="sphx-glr-download docutils container">
-<a class="reference download internal" download="" href="../_downloads/7bad39157ddf73bd946aceb8bc69eac4/auto_examples_jupyter.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">all</span> <span class="pre">examples</span> <span class="pre">in</span> <span class="pre">Jupyter</span> <span class="pre">notebooks:</span> <span class="pre">auto_examples_jupyter.zip</span></code></a></div>
+<a class="reference download internal" download="" href="../_downloads/195613ed69dc7de9449134bf862928f9/auto_examples_jupyter.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">all</span> <span class="pre">examples</span> <span class="pre">in</span> <span class="pre">Jupyter</span> <span class="pre">notebooks:</span> <span class="pre">auto_examples_jupyter.zip</span></code></a></div>
 </div>
 <p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.readthedocs.io">Gallery generated by Sphinx-Gallery</a></p>
 </div>

_downloads/434a24c66bbfd5f8398470a8c859e27b/plot_metric_learning_examples.ipynb renamed to _downloads/815de750020fca9d37949a373461ee38/plot_metric_learning_examples.ipynb

@@ -594,9 +594,9 @@ <h2>Metric Learning from Weaker Supervision<a class="headerlink" href="#metric-l
  [ 0  9]
  [ 0 10]
  ...
- [19  2]
- [42 40]
- [82 41]]
+ [35 23]
+ [52 64]
+ [86 63]]
 [ 1.  1.  1. ... -1. -1. -1.]
 </pre></div>
 </div>
@@ -633,12 +633,12 @@ <h2>Metric Learning from Weaker Supervision<a class="headerlink" href="#metric-l
 can also look at some <a class="reference internal" href="../introduction.html#use-cases"><span class="std std-ref">use cases</span></a> where you could combine
 metric-learn with scikit-learn estimators.</p>
 <p>This brings us to the end of this tutorial! Have fun Metric Learning :)</p>
-<p><strong>Total running time of the script:</strong> ( 1 minutes  19.674 seconds)</p>
+<p><strong>Total running time of the script:</strong> ( 1 minutes  15.365 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-metric-learning-examples-py">
 <div class="sphx-glr-download docutils container">
-<a class="reference download internal" download="" href="../_downloads/1701602fecf66f059913dd6559226da0/plot_metric_learning_examples.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_metric_learning_examples.py</span></code></a></div>
+<a class="reference download internal" download="" href="../_downloads/c2f7c5234b8d146a31cab6b296b2c2af/plot_metric_learning_examples.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_metric_learning_examples.py</span></code></a></div>
 <div class="sphx-glr-download docutils container">
-<a class="reference download internal" download="" href="../_downloads/434a24c66bbfd5f8398470a8c859e27b/plot_metric_learning_examples.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_metric_learning_examples.ipynb</span></code></a></div>
+<a class="reference download internal" download="" href="../_downloads/815de750020fca9d37949a373461ee38/plot_metric_learning_examples.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_metric_learning_examples.ipynb</span></code></a></div>
 </div>
 <p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.readthedocs.io">Gallery generated by Sphinx-Gallery</a></p>
 </div>

_downloads/1701602fecf66f059913dd6559226da0/plot_metric_learning_examples.py renamed to _downloads/c2f7c5234b8d146a31cab6b296b2c2af/plot_metric_learning_examples.py

@@ -264,12 +264,12 @@
   <span class="n">sandwich_demo</span><span class="p">()</span>
 </pre></div>
 </div>
-<p><strong>Total running time of the script:</strong> ( 0 minutes  4.620 seconds)</p>
+<p><strong>Total running time of the script:</strong> ( 0 minutes  4.099 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-sandwich-py">
 <div class="sphx-glr-download docutils container">
-<a class="reference download internal" download="" href="../_downloads/5632643bbb0183e351bf2b4dc2ab7c39/plot_sandwich.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_sandwich.py</span></code></a></div>
+<a class="reference download internal" download="" href="../_downloads/4228f005bd3fbc4a86c3ac1e76e0c591/plot_sandwich.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_sandwich.py</span></code></a></div>
 <div class="sphx-glr-download docutils container">
-<a class="reference download internal" download="" href="../_downloads/2bcf5e32b5d52d84565bc06c5ad91c98/plot_sandwich.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_sandwich.ipynb</span></code></a></div>
+<a class="reference download internal" download="" href="../_downloads/1fbc987b969eaee0a05177c2022570ae/plot_sandwich.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_sandwich.ipynb</span></code></a></div>
 </div>
 <p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.readthedocs.io">Gallery generated by Sphinx-Gallery</a></p>
 </div>

_images/sphx_glr_plot_metric_learning_examples_005.png

@@ -163,9 +163,23 @@ <h1>metric-learn: Metric Learning in Python<a class="headerlink" href="#metric-l
 <p><a class="reference external" href="https://github.com/scikit-learn-contrib/metric-learn">metric-learn</a>
 contains efficient Python implementations of several popular supervised and
 weakly-supervised metric learning algorithms. As part of <a class="reference external" href="https://github.com/scikit-learn-contrib">scikit-learn-contrib</a>, the API of metric-learn is compatible with <a class="reference external" href="https://scikit-learn.org/">scikit-learn</a>, the leading library for machine learning in
-Python. This allows to use of all the scikit-learn routines (for pipelining,
+Python. This allows to use all the scikit-learn routines (for pipelining,
 model selection, etc) with metric learning algorithms through a unified
 interface.</p>
+<p>If you use metric-learn in a scientific publication, we would appreciate
+citations to the following paper:</p>
+<p><a class="reference external" href="https://arxiv.org/abs/1908.04710">metric-learn: Metric Learning Algorithms in Python</a>, de Vazelhes
+<em>et al.</em>, arXiv:1908.04710, 2019.</p>
+<p>Bibtex entry:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@techreport</span><span class="p">{</span><span class="n">metric</span><span class="o">-</span><span class="n">learn</span><span class="p">,</span>
+  <span class="n">title</span> <span class="o">=</span> <span class="p">{</span><span class="n">metric</span><span class="o">-</span><span class="n">learn</span><span class="p">:</span> <span class="p">{</span><span class="n">M</span><span class="p">}</span><span class="n">etric</span> <span class="p">{</span><span class="n">L</span><span class="p">}</span><span class="n">earning</span> <span class="p">{</span><span class="n">A</span><span class="p">}</span><span class="n">lgorithms</span> <span class="ow">in</span> <span class="p">{</span><span class="n">P</span><span class="p">}</span><span class="n">ython</span><span class="p">},</span>
+  <span class="n">author</span> <span class="o">=</span> <span class="p">{{</span><span class="n">de</span> <span class="n">Vazelhes</span><span class="p">},</span> <span class="n">William</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Carey</span><span class="p">},</span> <span class="n">CJ</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Tang</span><span class="p">},</span> <span class="n">Yuan</span> <span class="ow">and</span>
+            <span class="p">{</span><span class="n">Vauquier</span><span class="p">},</span> <span class="n">Nathalie</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Bellet</span><span class="p">},</span> <span class="n">Aur</span><span class="p">{</span>\<span class="s1">&#39;e}lien},</span>
+  <span class="n">institution</span> <span class="o">=</span> <span class="p">{</span><span class="n">arXiv</span><span class="p">:</span><span class="mf">1908.04710</span><span class="p">},</span>
+  <span class="n">year</span> <span class="o">=</span> <span class="p">{</span><span class="mi">2019</span><span class="p">}</span>
+<span class="p">}</span>
+</pre></div>
+</div>
 <div class="section" id="documentation-outline">
 <h2>Documentation outline<a class="headerlink" href="#documentation-outline" title="Permalink to this headline">¶</a></h2>
 <div class="toctree-wrapper compound">