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README.md

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@@ -204,9 +204,10 @@ for input, target1, target2 in zip(inputs, task1_targets, task2_targets):
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optimizer.step()
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```
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Note that here, because the losses are a matrix instead of a simple vector, we compute a
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*generalized Gramian* and we extract weights from it using a
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[GeneralizedWeighting](https://torchjd.org/docs/aggregation/index.html#torchjd.aggregation.GeneralizedWeighting).
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> [!NOTE]
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> Here, because the losses are a matrix instead of a simple vector, we compute a *generalized
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> Gramian* and we extract weights from it using a
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> [GeneralizedWeighting](https://torchjd.org/docs/aggregation/index.html#torchjd.aggregation.GeneralizedWeighting).
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More usage examples can be found [here](https://torchjd.org/stable/examples/).
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