deploy: SimplexLab/TorchJD@8653929

Author: ValerianRey

latest/docs/aggregation/aligned_mtl/index.html

@@ -294,13 +294,18 @@
 <h1>Aligned-MTL<a class="headerlink" href="#aligned-mtl" title="Link to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torchjd.aggregation.AlignedMTL">
-<span class="property"><span class="k"><span class="pre">class</span></span><span class="w"> </span></span><span class="sig-prename descclassname"><span class="pre">torchjd.aggregation.</span></span><span class="sig-name descname"><span class="pre">AlignedMTL</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pref_vector</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/TorchJD/torchjd/blob/main/src/torchjd/aggregation/_aligned_mtl.py#L39-L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.AlignedMTL" title="Link to this definition">¶</a></dt>
+<span class="property"><span class="k"><span class="pre">class</span></span><span class="w"> </span></span><span class="sig-prename descclassname"><span class="pre">torchjd.aggregation.</span></span><span class="sig-name descname"><span class="pre">AlignedMTL</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pref_vector</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'min'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/TorchJD/torchjd/blob/main/src/torchjd/aggregation/_aligned_mtl.py#L41-L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.AlignedMTL" title="Link to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="../#torchjd.aggregation.Aggregator" title="torchjd.aggregation._aggregator_bases.Aggregator"><code class="xref py py-class docutils literal notranslate"><span class="pre">Aggregator</span></code></a> as defined in Algorithm 1 of
 <a class="reference external" href="https://openaccess.thecvf.com/content/CVPR2023/papers/Senushkin_Independent_Component_Alignment_for_Multi-Task_Learning_CVPR_2023_paper.pdf">Independent Component Alignment for Multi-Task Learning</a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>pref_vector</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> | <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></span>) – The preference vector to use.  If not provided, defaults to
-<span class="math notranslate nohighlight">\(\begin{bmatrix} \frac{1}{m} &amp; \dots &amp; \frac{1}{m} \end{bmatrix}^T \in \mathbb{R}^m\)</span>.</p>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>pref_vector</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> | <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></span>) – The preference vector to use.  If not provided, defaults to
+<span class="math notranslate nohighlight">\(\begin{bmatrix} \frac{1}{m} &amp; \dots &amp; \frac{1}{m} \end{bmatrix}^T \in \mathbb{R}^m\)</span>.</p></li>
+<li><p><strong>scale_mode</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Literal" title="(in Python v3.14)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Literal</span></code></a>[<code class="docutils literal notranslate"><span class="pre">'min'</span></code>, <code class="docutils literal notranslate"><span class="pre">'median'</span></code>, <code class="docutils literal notranslate"><span class="pre">'rmse'</span></code>]</span>) – The scaling mode used to build the balance transformation. <code class="docutils literal notranslate"><span class="pre">&quot;min&quot;</span></code> uses
+the smallest eigenvalue (default), <code class="docutils literal notranslate"><span class="pre">&quot;median&quot;</span></code> uses the median eigenvalue, and <code class="docutils literal notranslate"><span class="pre">&quot;rmse&quot;</span></code>
+uses the mean eigenvalue (as in the original implementation).</p></li>
+</ul>
 </dd>
 </dl>
 <div class="admonition note">
@@ -311,13 +316,18 @@ <h1>Aligned-MTL<a class="headerlink" href="#aligned-mtl" title="Link to this hea
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torchjd.aggregation.AlignedMTLWeighting">
-<span class="property"><span class="k"><span class="pre">class</span></span><span class="w"> </span></span><span class="sig-prename descclassname"><span class="pre">torchjd.aggregation.</span></span><span class="sig-name descname"><span class="pre">AlignedMTLWeighting</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pref_vector</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/TorchJD/torchjd/blob/main/src/torchjd/aggregation/_aligned_mtl.py#L64-L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.AlignedMTLWeighting" title="Link to this definition">¶</a></dt>
+<span class="property"><span class="k"><span class="pre">class</span></span><span class="w"> </span></span><span class="sig-prename descclassname"><span class="pre">torchjd.aggregation.</span></span><span class="sig-name descname"><span class="pre">AlignedMTLWeighting</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pref_vector</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'min'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/TorchJD/torchjd/blob/main/src/torchjd/aggregation/_aligned_mtl.py#L77-L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.AlignedMTLWeighting" title="Link to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="../#torchjd.aggregation.Weighting" title="torchjd.aggregation._weighting_bases.Weighting"><code class="xref py py-class docutils literal notranslate"><span class="pre">Weighting</span></code></a> giving the weights of
 <a class="reference internal" href="#torchjd.aggregation.AlignedMTL" title="torchjd.aggregation.AlignedMTL"><code class="xref py py-class docutils literal notranslate"><span class="pre">AlignedMTL</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>pref_vector</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> | <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></span>) – The preference vector to use. If not provided, defaults to
-<span class="math notranslate nohighlight">\(\begin{bmatrix} \frac{1}{m} &amp; \dots &amp; \frac{1}{m} \end{bmatrix}^T \in \mathbb{R}^m\)</span>.</p>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>pref_vector</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> | <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></span>) – The preference vector to use. If not provided, defaults to
+<span class="math notranslate nohighlight">\(\begin{bmatrix} \frac{1}{m} &amp; \dots &amp; \frac{1}{m} \end{bmatrix}^T \in \mathbb{R}^m\)</span>.</p></li>
+<li><p><strong>scale_mode</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Literal" title="(in Python v3.14)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Literal</span></code></a>[<code class="docutils literal notranslate"><span class="pre">'min'</span></code>, <code class="docutils literal notranslate"><span class="pre">'median'</span></code>, <code class="docutils literal notranslate"><span class="pre">'rmse'</span></code>]</span>) – The scaling mode used to build the balance transformation. <code class="docutils literal notranslate"><span class="pre">&quot;min&quot;</span></code> uses
+the smallest eigenvalue (default), <code class="docutils literal notranslate"><span class="pre">&quot;median&quot;</span></code> uses the median eigenvalue, and <code class="docutils literal notranslate"><span class="pre">&quot;rmse&quot;</span></code>
+uses the mean eigenvalue (as in the original implementation).</p></li>
+</ul>
 </dd>
 </dl>
 </dd></dl>