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‎latest/_sources/docs/scalarization/constant.rst.txt‎

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:hide-toc:
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Constant
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========
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.. autoclass:: torchjd.scalarization.Constant
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:members: __call__

‎latest/_sources/docs/scalarization/index.rst.txt‎

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scalarization
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=============
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.. automodule:: torchjd.scalarization
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:no-members:
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Abstract base class
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-------------------
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.. autoclass:: torchjd.scalarization.Scalarizer
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:members: __call__
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.. toctree::
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:hidden:
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:maxdepth: 1
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constant.rst
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mean.rst
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random.rst
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sum.rst

‎latest/_sources/docs/scalarization/mean.rst.txt‎

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:hide-toc:
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Mean
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====
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.. autoclass:: torchjd.scalarization.Mean
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:members: __call__

‎latest/_sources/docs/scalarization/random.rst.txt‎

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:hide-toc:
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Random
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======
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.. autoclass:: torchjd.scalarization.Random
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:members: __call__

‎latest/_sources/docs/scalarization/sum.rst.txt‎

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Sum
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===
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.. autoclass:: torchjd.scalarization.Sum
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‎latest/_sources/index.rst.txt‎

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:doc:`Aggregation <docs/aggregation/index>`, we provide an overview of the various aggregators
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available in TorchJD, and their corresponding weightings.
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For comparison against simple baselines, the :doc:`Scalarization <docs/scalarization/index>`
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package provides scalarizers that combine a tensor of losses into a single scalar loss, allowing
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standard gradient descent to be used.
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A straightforward application of Jacobian descent is multi-task learning, in which the vector of
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per-task losses has to be minimized. To start using TorchJD for multi-task learning, follow our
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:doc:`MTL example <examples/mtl>`.
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docs/autogram/index.rst
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docs/autojac/index.rst
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docs/scalarization/index.rst
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docs/linalg/index.rst

‎latest/docs/aggregation/aligned_mtl/index.html‎

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<li class="toctree-l2"><a class="reference internal" href="../trimmed_mean/">Trimmed Mean</a></li>
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</ul>
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</li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../../linalg/">linalg</a><input aria-label="Toggle navigation of linalg" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><span class="icon"><svg><use href="#svg-arrow-right"></use></svg></span></label><ul>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../../scalarization/">scalarization</a><input aria-label="Toggle navigation of scalarization" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><span class="icon"><svg><use href="#svg-arrow-right"></use></svg></span></label><ul>
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<li class="toctree-l2"><a class="reference internal" href="../../scalarization/constant/">Constant</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../scalarization/mean/">Mean</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../scalarization/random/">Random</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../scalarization/sum/">Sum</a></li>
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</ul>
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</li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../../linalg/">linalg</a><input aria-label="Toggle navigation of linalg" class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><span class="icon"><svg><use href="#svg-arrow-right"></use></svg></span></label><ul>
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<li class="toctree-l2"><a class="reference internal" href="../../linalg/matrix/">Matrix</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../linalg/psd_matrix/">PSDMatrix</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../linalg/dual_cone/">Dual Cone Projectors</a></li>
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<h1>Aligned-MTL<a class="headerlink" href="#aligned-mtl" title="Link to this heading"></a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="torchjd.aggregation.AlignedMTL">
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<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/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_aligned_mtl.py#L90-L139"><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>
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<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/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_aligned_mtl.py#L91-L140"><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>
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<dd><p><a class="reference internal" href="../#torchjd.aggregation.GramianWeightedAggregator" title="torchjd.aggregation.GramianWeightedAggregator"><code class="xref py py-class docutils literal notranslate"><span class="pre">GramianWeightedAggregator</span></code></a> as defined in Algorithm 1 of
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<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>
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<dl class="field-list simple">
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<dt class="sig sig-object py" id="torchjd.aggregation.AlignedMTLWeighting">
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<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/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_aligned_mtl.py#L20-L87"><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>
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<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/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_aligned_mtl.py#L21-L88"><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>
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<dd><p><a class="reference internal" href="../#torchjd.aggregation.Weighting" title="torchjd.aggregation.Weighting"><code class="xref py py-class docutils literal notranslate"><span class="pre">Weighting</span></code></a> [<a class="reference internal" href="../../linalg/psd_matrix/#torchjd.linalg.PSDMatrix" title="torchjd.linalg.PSDMatrix"><code class="xref py py-class docutils literal notranslate"><span class="pre">PSDMatrix</span></code></a>]
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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>
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<dl class="field-list simple">

‎latest/docs/aggregation/cagrad/index.html‎

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<li class="toctree-l2"><a class="reference internal" href="../trimmed_mean/">Trimmed Mean</a></li>
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</ul>
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</li>
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<li class="toctree-l2"><a class="reference internal" href="../../scalarization/constant/">Constant</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../scalarization/mean/">Mean</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../scalarization/random/">Random</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../scalarization/sum/">Sum</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../linalg/matrix/">Matrix</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../linalg/psd_matrix/">PSDMatrix</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../linalg/dual_cone/">Dual Cone Projectors</a></li>

‎latest/docs/aggregation/config/index.html‎

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<li class="toctree-l1 has-children"><a class="reference internal" href="../../scalarization/">scalarization</a><input aria-label="Toggle navigation of scalarization" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><span class="icon"><svg><use href="#svg-arrow-right"></use></svg></span></label><ul>
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<li class="toctree-l2"><a class="reference internal" href="../../scalarization/constant/">Constant</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../scalarization/mean/">Mean</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../scalarization/random/">Random</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../scalarization/sum/">Sum</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../linalg/matrix/">Matrix</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../linalg/psd_matrix/">PSDMatrix</a></li>
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<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">ConFIG</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/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_config.py#L17-L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.ConFIG" title="Link to this definition"></a></dt>
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<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">ConFIG</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/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_config.py#L18-L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.ConFIG" title="Link to this definition"></a></dt>
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<dd><p><a class="reference internal" href="../#torchjd.aggregation.Aggregator" title="torchjd.aggregation.Aggregator"><code class="xref py py-class docutils literal notranslate"><span class="pre">Aggregator</span></code></a> as defined in Equation 2 of <a class="reference external" href="https://arxiv.org/pdf/2408.11104">ConFIG:
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Towards Conflict-free Training of Physics Informed Neural Networks</a>.</p>
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