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diff --git a/dev/_downloads/01119b09b904483a657e921d21bc15d1/demo_pca_via_affinity_matcher.ipynb b/dev/_downloads/01119b09b904483a657e921d21bc15d1/demo_pca_via_affinity_matcher.ipynb
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diff --git a/dev/_downloads/9c98a10e6c0743947672bcab8d552167/demo_pca_via_affinity_matcher.zip b/dev/_downloads/9c98a10e6c0743947672bcab8d552167/demo_pca_via_affinity_matcher.zip
diff --git a/dev/_downloads/a326ed5e0cfd78a82e54fd5203079d4a/demo_tsne_swiss_roll.zip b/dev/_downloads/a326ed5e0cfd78a82e54fd5203079d4a/demo_tsne_swiss_roll.zip
diff --git a/dev/_downloads/ccd90bc6ee7cbcb51bd8b71882fb5168/demo_ne_methods_affinity_matcher.zip b/dev/_downloads/ccd90bc6ee7cbcb51bd8b71882fb5168/demo_ne_methods_affinity_matcher.zip
diff --git a/dev/_downloads/d07a5a618e87545c420604821fa60541/demo_tsne_swiss_roll.ipynb b/dev/_downloads/d07a5a618e87545c420604821fa60541/demo_tsne_swiss_roll.ipynb
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@@ -187,7 +185,7 @@ <h1>Source code for torchdr.affinity.entropic</h1><div class="highlight"><pre>
 
 
 <span class="k">def</span> <span class="nf">_bounds_entropic_affinity</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">perplexity</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Compute the bounds derived in [4]_ for the entropic affinity root.</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Compute the bounds derived in [V13]_ for the entropic affinity root.</span>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
@@ -206,7 +204,7 @@ <h1>Source code for torchdr.affinity.entropic</h1><div class="highlight"><pre>
 
 <span class="sd">    References</span>
 <span class="sd">    ----------</span>
-<span class="sd">    .. [4]  Max Vladymyrov, Miguel A. Carreira-Perpinan (2013).</span>
+<span class="sd">    .. [V13]  Max Vladymyrov, Miguel A. Carreira-Perpinan (2013).</span>
 <span class="sd">            Entropic Affinities: Properties and Efficient Numerical Computation.</span>
 <span class="sd">            International Conference on Machine Learning (ICML).</span>
 <span class="sd">    &quot;&quot;&quot;</span>
@@ -272,7 +270,7 @@ <h1>Source code for torchdr.affinity.entropic</h1><div class="highlight"><pre>
 <div class="viewcode-block" id="EntropicAffinity">
 <a class="viewcode-back" href="../../../gen_modules/torchdr.EntropicAffinity.html#torchdr.EntropicAffinity">[docs]</a>
 <span class="k">class</span> <span class="nc">EntropicAffinity</span><span class="p">(</span><span class="n">SparseLogAffinity</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Solve the directed entropic affinity problem introduced in [1]_.</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Solve the directed entropic affinity problem introduced in [H02]_.</span>
 
 <span class="sd">    The algorithm computes the optimal dual variable</span>
 <span class="sd">    :math:`\mathbf{\varepsilon}^* \in \mathbb{R}^n_{&gt;0}` such that</span>
@@ -290,7 +288,7 @@ <h1>Source code for torchdr.affinity.entropic</h1><div class="highlight"><pre>
 <span class="sd">    since rows of :math:`\mathbf{P}^{\mathrm{e}}` are independent subproblems.</span>
 
 <span class="sd">    **Convex problem.** Corresponds to the matrix :math:`\mathbf{P}^{\mathrm{e}}`</span>
-<span class="sd">    in [3]_, solving the convex optimization problem</span>
+<span class="sd">    in [V23]_, solving the convex optimization problem</span>
 
 <span class="sd">    .. math::</span>
 <span class="sd">        \mathbf{P}^{\mathrm{e}} \in \mathop{\arg\min}_{\mathbf{P} \in \mathbb{R}_+^{n \times n}} \: &amp;\langle \mathbf{C}, \mathbf{P} \rangle \\</span>
@@ -311,7 +309,7 @@ <h1>Source code for torchdr.affinity.entropic</h1><div class="highlight"><pre>
 
 <span class="sd">    .. note:: A symmetric version is also available at</span>
 <span class="sd">        :class:`~torchdr.SymmetricEntropicAffinity`. It is the affinity matrix</span>
-<span class="sd">        used in :class:`~SNEkhorn`/ :class:`~TSNEkhorn` [3]_. In TSNE [2]_,</span>
+<span class="sd">        used in :class:`~SNEkhorn`/ :class:`~TSNEkhorn` [V23]_. In TSNE [V08]_,</span>
 <span class="sd">        the entropic affinity is simply averaged with its transpose.</span>
 
 <span class="sd">    Parameters</span>
@@ -339,15 +337,15 @@ <h1>Source code for torchdr.affinity.entropic</h1><div class="highlight"><pre>
 
 <span class="sd">    References</span>
 <span class="sd">    ----------</span>
-<span class="sd">    .. [1]  Geoffrey Hinton, Sam Roweis (2002).</span>
+<span class="sd">    .. [H02]  Geoffrey Hinton, Sam Roweis (2002).</span>
 <span class="sd">            Stochastic Neighbor Embedding.</span>
 <span class="sd">            Advances in neural information processing systems 15 (NeurIPS).</span>
 
-<span class="sd">    .. [2]  Laurens van der Maaten, Geoffrey Hinton (2008).</span>
+<span class="sd">    .. [V08]  Laurens van der Maaten, Geoffrey Hinton (2008).</span>
 <span class="sd">            Visualizing Data using t-SNE.</span>
 <span class="sd">            The Journal of Machine Learning Research 9.11 (JMLR).</span>
 
-<span class="sd">    .. [3]  Hugues Van Assel, Titouan Vayer, Rémi Flamary, Nicolas Courty (2023).</span>
+<span class="sd">    .. [V23]  Hugues Van Assel, Titouan Vayer, Rémi Flamary, Nicolas Courty (2023).</span>
 <span class="sd">            SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities.</span>
 <span class="sd">            Advances in Neural Information Processing Systems 36 (NeurIPS).</span>
 <span class="sd">    &quot;&quot;&quot;</span>  <span class="c1"># noqa: E501</span>
@@ -390,7 +388,7 @@ <h1>Source code for torchdr.affinity.entropic</h1><div class="highlight"><pre>
             <span class="k">return</span> <span class="kc">False</span>
 
     <span class="k">def</span> <span class="nf">_compute_sparse_log_affinity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
-<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Solve the problem (EA) in [1]_ to compute the entropic affinity matrix.</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Solve the problem (EA) in [H02]_ to compute the entropic affinity matrix.</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
@@ -466,7 +464,7 @@ <h1>Source code for torchdr.affinity.entropic</h1><div class="highlight"><pre>
 <div class="viewcode-block" id="SymmetricEntropicAffinity">
 <a class="viewcode-back" href="../../../gen_modules/torchdr.SymmetricEntropicAffinity.html#torchdr.SymmetricEntropicAffinity">[docs]</a>
 <span class="k">class</span> <span class="nc">SymmetricEntropicAffinity</span><span class="p">(</span><span class="n">LogAffinity</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Compute the symmetric entropic affinity (SEA) introduced in [3]_.</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Compute the symmetric entropic affinity (SEA) introduced in [V23]_.</span>
 
 <span class="sd">    Compute the solution :math:`\mathbf{P}^{\mathrm{se}}` to the symmetric entropic</span>
 <span class="sd">    affinity (SEA) problem described in [3]_.</span>
@@ -537,7 +535,7 @@ <h1>Source code for torchdr.affinity.entropic</h1><div class="highlight"><pre>
 
 <span class="sd">    References</span>
 <span class="sd">    ----------</span>
-<span class="sd">    .. [3] SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities,</span>
+<span class="sd">    .. [V23] SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities,</span>
 <span class="sd">        Hugues Van Assel, Titouan Vayer, Rémi Flamary, Nicolas Courty, NeurIPS 2023.</span>
 <span class="sd">    &quot;&quot;&quot;</span>  <span class="c1"># noqa: E501</span>
 
@@ -572,7 +570,7 @@ <h1>Source code for torchdr.affinity.entropic</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">eps_square</span> <span class="o">=</span> <span class="n">eps_square</span>
 
     <span class="k">def</span> <span class="nf">_compute_log_affinity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
-<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Solve the problem (SEA) in [3]_.</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Solve the problem (SEA) in [V23]_.</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
@@ -730,7 +728,7 @@ <h1>Source code for torchdr.affinity.entropic</h1><div class="highlight"><pre>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Compute the symmetric doubly stochastic affinity matrix.</span>
 
 <span class="sd">    The algorithm computes the doubly stochastic matrix :math:`\mathbf{P}^{\mathrm{ds}}`</span>
-<span class="sd">    with controlled global entropy using the symmetric Sinkhorn algorithm [5]_.</span>
+<span class="sd">    with controlled global entropy using the symmetric Sinkhorn algorithm [S67]_.</span>
 
 <span class="sd">    The algorithm computes the optimal dual variable</span>
 <span class="sd">    :math:`\mathbf{f}^\star \in \mathbb{R}^n` such that</span>
@@ -744,9 +742,9 @@ <h1>Source code for torchdr.affinity.entropic</h1><div class="highlight"><pre>
 <span class="sd">    - :math:`\varepsilon`: entropic regularization parameter.</span>
 <span class="sd">    - :math:`\mathbf{1} := (1,...,1)^\top`: all-ones vector.</span>
 
-<span class="sd">    :math:`\mathbf{f}^\star` is computed by performing dual ascent via the Sinkhorn fixed-point iteration (eq. 25 in [7]_).</span>
+<span class="sd">    :math:`\mathbf{f}^\star` is computed by performing dual ascent via the Sinkhorn fixed-point iteration (eq. 25 in [F19]_).</span>
 
-<span class="sd">    **Convex problem.** Consists in solving the following symmetric entropic optimal transport problem [6]_:</span>
+<span class="sd">    **Convex problem.** Consists in solving the following symmetric entropic optimal transport problem [C13]_:</span>
 
 <span class="sd">    .. math::</span>
 <span class="sd">        \mathbf{P}^{\mathrm{ds}} \in \mathop{\arg\min}_{\mathbf{P} \in \mathcal{DS}} \: \langle \mathbf{C}, \mathbf{P} \rangle + \varepsilon \mathrm{H}(\mathbf{P})</span>
@@ -796,15 +794,15 @@ <h1>Source code for torchdr.affinity.entropic</h1><div class="highlight"><pre>
 
 <span class="sd">    References</span>
 <span class="sd">    ----------</span>
-<span class="sd">    .. [5]  Richard Sinkhorn, Paul Knopp (1967).</span>
+<span class="sd">    .. [S67]  Richard Sinkhorn, Paul Knopp (1967).</span>
 <span class="sd">            Concerning nonnegative matrices and doubly stochastic matrices.</span>
 <span class="sd">            Pacific Journal of Mathematics, 21(2), 343-348.</span>
 
-<span class="sd">    .. [6]  Marco Cuturi (2013).</span>
+<span class="sd">    .. [C13]  Marco Cuturi (2013).</span>
 <span class="sd">            Sinkhorn Distances: Lightspeed Computation of Optimal Transport.</span>
 <span class="sd">            Advances in Neural Information Processing Systems 26 (NeurIPS).</span>
 
-<span class="sd">    .. [7]  Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-ichi Amari,</span>
+<span class="sd">    .. [F19]  Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-ichi Amari,</span>
 <span class="sd">            Alain Trouvé, Gabriel Peyré (2019).</span>
 <span class="sd">            Interpolating between Optimal Transport and MMD using Sinkhorn Divergences.</span>
 <span class="sd">            International Conference on Artificial Intelligence and Statistics</span>