deploy: SimplexLab/TorchJD@25b44f0

Author: ValerianRey

latest/_sources/examples/index.rst.txt

@@ -20,8 +20,7 @@ This section contains some usage examples for TorchJD.
   dedicated backpropagation function :doc:`mtl_backward <../docs/autojac/mtl_backward>`.
 - :doc:`Instance-Wise Multi-Task Learning (IWMTL) <iwmtl>` shows how to combine multi-task learning
   with instance-wise risk minimization: one loss per task and per element of the batch, using the
-  :doc:`autogram.Engine <../docs/autogram/engine>` and a :doc:`GeneralizedWeighting
-  <../docs/aggregation/index>`.
+  :doc:`autogram.Engine <../docs/autogram/engine>`.
 - :doc:`Recurrent Neural Network (RNN) <rnn>` shows how to apply Jacobian descent to RNN training,
   with one loss per output sequence element.
 - :doc:`Monitoring Aggregations <monitoring>` shows how to monitor the aggregation performed by the

latest/examples/index.html

@@ -326,7 +326,7 @@ <h1>Examples<a class="headerlink" href="#examples" title="Link to this heading">
 dedicated backpropagation function <a class="reference internal" href="../docs/autojac/mtl_backward/"><span class="doc">mtl_backward</span></a>.</p></li>
 <li><p><a class="reference internal" href="iwmtl/"><span class="doc">Instance-Wise Multi-Task Learning (IWMTL)</span></a> shows how to combine multi-task learning
 with instance-wise risk minimization: one loss per task and per element of the batch, using the
-<a class="reference internal" href="../docs/autogram/engine/"><span class="doc">autogram.Engine</span></a> and a <a class="reference internal" href="../docs/aggregation/"><span class="doc">GeneralizedWeighting</span></a>.</p></li>
+<a class="reference internal" href="../docs/autogram/engine/"><span class="doc">autogram.Engine</span></a>.</p></li>
 <li><p><a class="reference internal" href="rnn/"><span class="doc">Recurrent Neural Network (RNN)</span></a> shows how to apply Jacobian descent to RNN training,
 with one loss per output sequence element.</p></li>
 <li><p><a class="reference internal" href="monitoring/"><span class="doc">Monitoring Aggregations</span></a> shows how to monitor the aggregation performed by the